# data[].attributes.body
A myofibric tissue that is constructed from a triculture of myoblasts, endothelial cells, and fibroblasts seeded onto 3D biodegradable scaffolds. The grafts are cultured in vitro for up to 3 weeks to allow for myotube assembly from myoblasts. Endothelial cells and fibroblasts are added to support vascular integration into the host and to enhance the host angiogenic response after transplantation to a murine muscle defect.

T. Kaufman et al., ‘Innervation of an engineered muscle graft for reconstruction of muscle defects’, American Journal of Transplantation, vol. 19, no. 1, pp. 37–47, 2019.

https://www.sciencedirect.com/science/article/pii/S1600613522088979

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Innervation of an engineered muscle graft for reconstruction of muscle defects

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Autologous muscle flaps are commonly used to reconstruct defects that involve muscle impairment. To maintain viability and functionality of these flap...

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the biomaterial harvested from a patient’s healthy muscles and transplanted to the site of injury
to overcome the consequences of a significant tissue damage.  Although this method is efficient, it has several drawbacks, such as donor site morbidity and limited muscle availability.

T. Kaufman et al., ‘Innervation of an engineered muscle graft for reconstruction of muscle defects’, American Journal of Transplantation, vol. 19, no. 1, pp. 37–47, 2019.

https://www.sciencedirect.com/science/article/pii/S1600613522088979

# data[].attributes.attachment_link.title
Innervation of an engineered muscle graft for reconstruction of muscle defects

# data[].attributes.attachment_link.description
Autologous muscle flaps are commonly used to reconstruct defects that involve muscle impairment. To maintain viability and functionality of these flap...

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Regenerative Peripheral Nerve Interface (RPNI)

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Regenerative Peripheral Nerve Interface (RPNI)

# data[].attributes.body
a neurosurgical approach  that implies a small, autologous partial muscle graft being reinnervated by a transected peripheral nerve branch. Following initial implantation, the muscle graft temporarily degenerates due to lack of innervation and vascularization. During this time, cells in the graft periphery are sustained via diffusion from the surrounding tissue [15]. Over the course of several months, the graft is revascularized, regenerates (creating new, healthy muscle fibers), and is reinnervated by the transplanted nerve through axonal sprouting and elongation within the graft.

Z. T. Irwin et al., ‘Chronic recording of hand prosthesis control signals via a regenerative peripheral nerve interface in a rhesus macaque’, Journal of neural engineering, vol. 13, no. 4, p. 046007, 2016.

https://sci-hub.ru/10.1088/1741-2560/13/4/046007

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Affiliations

¹ Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland.

² SensArs Neuroprosthetics, Lausanne CH-1004, Switzerland.

³ Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015 Switzerland.

⁴ The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56025, Italy.

⁵ Orthopaedic Surgery Department, School of Medicine, University of Belgrade, 11000 Belgrade, Serbia.

⁶ Clinic of Orthopedic Surgery and Traumatology, Clinical Centre Of Serbia, 11000 Belgrade, Serbia.

⁷ NearLab-Neuroengineering and Medical Robotics Laboratory Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, Italy.

⁸ Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, University of Freiburg-IMTEK, 79110 Freiburg, Germany.

⁹ INRIA Camin Team, University of Montpellier-LIRMM, F-34095 Montpellier, France.

¹⁰ mBrainTrain d.o.o., 11000 Belgrade, Serbia.

¹¹ Össur hf., R&D, 110 Reykjavik, Iceland.

¹² Axonic, 06220 Vallauris, France.

¹³ Specialized hospital for rehabilitation and orthopaedic prosthetics, 11000 Belgrade, Serbia.

¹⁴ Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland. stanisa.raspopovic@hest.ethz.ch.

Affiliations

¹ Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland.

² SensArs Neuroprosthetics, Lausanne CH-1004, Switzerland.

³ Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015 Switzerland.

⁴ The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56025, Italy.

⁵ Orthopaedic Surgery Department, School of Medicine, University of Belgrade, 11000 Belgrade, Serbia.

⁶ Clinic of Orthopedic Surgery and Traumatology, Clinical Centre Of Serbia, 11000 Belgrade, Serbia.

⁷ NearLab-Neuroengineering and Medical Robotics Laboratory Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, Italy.

⁸ Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, University of Freiburg-IMTEK, 79110 Freiburg, Germany.

⁹ INRIA Camin Team, University of Montpellier-LIRMM, F-34095 Montpellier, France.

¹⁰ mBrainTrain d.o.o., 11000 Belgrade, Serbia.

¹¹ Össur hf., R&D, 110 Reykjavik, Iceland.

¹² Axonic, 06220 Vallauris, France.

¹³ Specialized hospital for rehabilitation and orthopaedic prosthetics, 11000 Belgrade, Serbia.

¹⁴ Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland. stanisa.raspopovic@hest.ethz.ch.

Abstract

Lower limb amputation (LLA) destroys the sensory communication between the brain and the external world during standing and walking. Current prostheses do not restore sensory feedback to amputees, who, relying on very limited haptic information from the stump-socket interaction, are forced to deal with serious issues: the risk of falls, decreased mobility, prosthesis being perceived as an external object (low embodiment), and increased cognitive burden. Poor mobility is one of the causes of eventual device abandonment. Restoring sensory feedback from the missing leg of above-knee (transfemoral) amputees and integrating the sensory feedback into the sensorimotor loop would markedly improve the life of patients. In this study, we developed a leg neuroprosthesis, which provided real-time tactile and emulated proprioceptive feedback to three transfemoral amputees through nerve stimulation. The feedback was exploited in active tasks, which proved that our approach promoted improved mobility, fall prevention, and agility. We also showed increased embodiment of the lower limb prosthesis (LLP), through phantom leg displacement perception and questionnaires, and ease of the cognitive effort during a dual-task paradigm, through electroencephalographic recordings. Our results demonstrate that induced sensory feedback can be integrated at supraspinal levels to restore functional abilities of the missing leg. This work paves the way for further investigations about how the brain interprets different artificial feedback strategies and for the development of fully implantable sensory-enhanced leg neuroprostheses, which could drastically ameliorate life quality in people with disability.

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national center for biotechnology information

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Enhancing functional abilities and cognitive integration of the lower limb prosthesis - PubMed

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Lower limb amputation (LLA) destroys the sensory communication between the brain and the external world during standing and walking. Current prostheses do not restore sensory feedback to amputees, who, relying on very limited haptic information from the stump-socket interaction, are forced to deal w ...

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is the method to record impulse traffic in peripheral nerves of human subjects.

A. B. Vallbo, K.-E. Hagbarth, and B. G. Wallin, ‘Microneurography: how the technique developed and its role in the investigation of the sympathetic nervous system’, Journal of applied physiology, vol. 96, no. 4, pp. 1262–1269, 2004.

https://journals.physiology.org/doi/full/10.1152/japplphysiol.00470.2003

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R. Romo and V. de Lafuente, ‘Conversion of sensory signals into perceptual decisions’, Progress in neurobiology, vol. 103, pp. 41–75, 2013.

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R. Romo and V. de Lafuente, ‘Conversion of sensory signals into perceptual decisions’, Progress in neurobiology, vol. 103, pp. 41–75, 2013.

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R. Romo and V. de Lafuente, ‘Conversion of sensory signals into perceptual decisions’, Progress in neurobiology, vol. 103, pp. 41–75, 2013.

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is a pathological wound healing in which connective tissue replaces normal parenchymal tissue to the extent that it goes unchecked, leading to considerable tissue remodelling and the formation of permanent scar tissue.

T. A. Wynn, ‘Fibrotic disease and the TH1/TH2 paradigm’, Nature Reviews Immunology, vol. 4, no. 8, pp. 583–594, 2004.

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the process of neural and behavioral reorganization following sensory loss is known as sensory compensation.

M. Pieniak, K. Lachowicz-Tabaczek, M. Karwowski, and A. Oleszkiewicz, ‘Sensory compensation beliefs among blind and sighted individuals’, Scandinavian journal of psychology, vol. 63, no. 1, pp. 72–82, 2022.

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process that leads to integration of a foreign object into the pre-existent neural infrastructure supporting the body

J. Zbinden, E. Lendaro, and M. Ortiz-Catalan, ‘Prosthetic embodiment: systematic review on definitions, measures, and experimental paradigms’, Journal of NeuroEngineering and Rehabilitation, vol. 19, no. 1, p. 37, 2022.

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an adapting stimulus (either 5 or 0.5 s in duration), (2) a standard stimulus (0.5 s) delivered at the same site as the adapting stimulus, and
(3) a test stimulus (0.5 s) delivered to a skin site different from
the standard stimulus. Duration of the inter-stimulus (ISI) and
inter-trial (ITI) intervals was held constant for all runs at 2 and
30 s, respectively

V. Tannan, B. L. Whitsel, and M. A. Tommerdahl, ‘Vibrotactile adaptation enhances spatial localization’, Brain research, vol. 1102, no. 1, pp. 109–116, 2006.

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sensory neurons that respond to innocuous mechanical stimuli.

L. L. Orefice, ‘Peripheral somatosensory neuron dysfunction: emerging roles in autism spectrum disorders’, Neuroscience, vol. 445, pp. 120–129, 2020.

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is followed by significant improvement of the capacity of
subjects to discriminate the amplitude and frequency of
vibrotactile stimuli when the frequencies of the adapting
and standard stimuli are similar

V. Tannan, B. L. Whitsel, and M. A. Tommerdahl, ‘Vibrotactile adaptation enhances spatial localization’, Brain research, vol. 1102, no. 1, pp. 109–116, 2006.

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High- threshold mechanoreceptor neurons (HTMRs) 

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High- threshold mechanoreceptor neurons (HTMRs) 

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sensory neurons that encode harmful mechanical stimuli

L. L. Orefice, ‘Peripheral somatosensory neuron dysfunction: emerging roles in autism spectrum disorders’, Neuroscience, vol. 445, pp. 120–129, 2020.

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resulting neural signal from action potentials of many axons that comes as superposition of the action potentials (AP).  In this case the amplitude of the eCAP will be much larger than that of a single AP (∼100 µV for cuffs), see figure 6(a) for an exemplar recording of both spontaneous APs and eCAPs.

R. G. L. Koh, J. Zariffa, L. Jabban, S.-C. Yen, N. Donaldson, and B. W. Metcalfe, ‘Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system’, Journal of Neural Engineering, 2022.

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*
Journal of Neural Engineering , Volume 19 , Number 4 Citation Ryan G L Koh DOI 10.1088/1741-2552/ac7d74 et al 2022 J. Neural Eng. 19 042001

Author affiliations

¹ KITE, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, M5G 2A2, Canada

² Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada

³ Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M4G 3V9, Canada

⁴ Rehabilitation Sciences Institute, University of Toronto, Toronto, ON M5S 2E4, Canada

⁵ Centre for Biosensors, Bioelectronics, and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom

⁶ Innovation and Design Programme, Faculty of Engineering, National University of Singapore, Singapore, Singapore

⁷ The N.1 Institute for Health, National University of Singapore, Singapore, Singapore

⁸ Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6DH, United Kingdom

Author notes

⁹ Author to whom any correspondence should be addressed.

ORCID iDs

Buy this article in print

1741-2552/19/4/042001

Abstract

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of PNIs, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

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Neural control is at the heart of our agency in the world. Through a combination of conscious and automatic processes, the nervous system enables us to regulate ourselves and our interactions with our environment. As we seek to modulate, restore, or enhance these abilities, we have turned in recent decades to technologies that can interface effectively with the nervous system. Certain applications benefit from interfaces at the level of the central nervous system (CNS), for example, when dealing with brain disorders such as Parkinson's, psychiatric conditions [ 1, 2] or attempting to coordinate patterns of movement through spinal circuitry [ 3, 4]. In many cases, however, the peripheral nervous system (PNS) offers the advantage of carrying afferent or efferent information that most directly relates to the function of the target of interest.

For most of its history, this field has focused on restoring function lost as a result of amputation [ 5- 7] or paralysis [ 8, 9]. More recently, the scope has broadened considerably (as reviewed in [ 10]), including significant interest in chronic disease applications involving the autonomic nervous system [ 11- 13]. These developments have gone hand-in-hand with new progress in neural interfacing technologies and have spawned the related field of bioelectronic medicine.

Peripheral nerve interfaces (PNIs) can modulate neural activity through stimulation and/or monitor neural control and feedback through recording. Key potential applications of peripheral nerve recordings include the control of prosthetic limbs through extracted motor commands [ 14], the closed-loop control of functional electrical stimulation (FES) systems through extracted afferent information [ 15], and the neuromodulation of body functions through the identification of autonomic control signal [ 16] and electrical disease biomarkers [ 17].

Achieving stable and functional recording in the PNS has been a persistent challenge in neural engineering for several reasons-many of which are distinct from those faced by CNS interfaces. First, in peripheral nerves, the nerve fibres (axons) are tightly packed into fascicles, which are in turn held together by connective tissue to form the nerve trunk [ 18]. The whole structure, in many cases, has a diameter on the order of hundreds of microns to a few millimetres. In this configuration, the axons are densely organised into a relatively small-diameter structure, making it difficult to isolate activity related to a particular function. Second, sparse firing patterns and the lack of large, synchronised populations result in small-amplitude signals. Third, many peripheral nerves experience significant movement during physical activities (e.g. during respiration in larger mammals), and are often located close to muscles whose bioelectrical activity creates substantial interference. Lastly, in the case of chronic implantation, encapsulation tissue can form in and around an electrode and alter the nerve-electrode interface and thus the amplitude and nature of the recorded signals [ 19].

Several device designs have been proposed to create reliable PNIs and these are divided into intraneural and extraneural. The former involves penetration of the device into the nerve trunk, while the latter relies on devices positioned on or near the surface of the trunk [ 20, 21]. These approaches have been considered in a trade-off between selectivity and invasiveness.

Regardless of the approach, multi-channel PNI designs are increasingly providing new possibilities to extract detailed information about the neural function. The availability of multiple channels provides signal processing opportunities for resolving ambiguities that cannot be dealt with in a single channel, and enables powerful machine learning or regression approaches [ 14, 22- 29]. Several recent reviews have covered peripheral nerve electrode designs [ 20, 21, 30, 31], but reviews of the data analysis techniques necessary for the creation of effective PNIs are limited [ 32].

The objective of this tutorial article is to provide a resource that captures the key methods for the analysis of peripheral nerve recordings, and specifically examines the critical interplay between the type of electrode used, the signal analysis techniques applied, and the nature of the information extracted. Further guidance is also given on the importance of appropriate experimental models and the challenges associated with chronic implantation.

The structure of this paper is as follows. Section 2 will introduce general principles and typical approaches to making recordings. Section 3 will introduce the most common electrode geometries and discuss their impact on the nature of information extracted, section 4 will describe the most common signal processing approaches, section 5 will discuss experimental and translational considerations, and section 6 will identify future trends and technologies.

It is important to understand the general principles of a PNI recording system before considering the relative merits afforded by different configurations. The following sections will briefly introduce the properties of peripheral nerve recordings as well as general principles in acquiring and pre-processing these signals.

Figure 1 shows a simplified overview of a typical PNI. Electrodes are placed near, on, or in, the nerve trunk and are connected to low-noise amplifiers (LNAs) and a digital signal processing (DSP) system. The neural signals occurring within the nerve are thus amplified, digitised, and processed to provide an output that can take several forms including: spike trains, continuous time series, and images showing the source of the neural activity within the nerve trunk. This information can then be used to inform a prosthesis such as an artificial limb or a neuromodulation device. It is important first to have an understanding of the nature of neural signals, and so a brief overview will now be given.

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2.1. Biophysics of neural signals

Individual axons produce action currents with magnitudes in the pico-ampere range, due to APs, which may be detected as neural signals. These small action currents give rise to small potentials (on the order of 1 µ V), that are difficult to detect in the presence of noise and interference. There are two ways to obtain a detectable potential: an electrode must be tiny and in close proximity to the axon, so that the potential is produced across the spreading resistance from a node of Ranvier, or there must be a restricted extra-cellular space that creates a high resistance through which the small action currents flow. These two cases give rise to a taxonomy wherein an interface may be defined as operating with either unrestricted (e.g. intraneural) or restricted (e.g. extraneural) extracellular space.

Neural signals that occur spontaneously (i.e. without external stimulation or modulation) are composed of individual APs resulting from normal biological functions. Typically, neurons within different fascicles innervate unrelated tissue, and do not fire synchronously. Consequently, the observed neural signal is characterised by low amplitude and high frequency activity.

Spontaneous signals recorded extraneurally (i.e. within a constrained extracellular space) from the surface of the nerve trunk are rarely larger than 30 µV peak-to-peak [ 33], whereas intraneural signals recorded from inside the trunk can be over 100 µV peak-to-peak [ 34]. Most of the signal power is concentrated in the range of 300 Hz-5 kHz, with the peak below 3 kHz [ 35- 37].

The small amplitude of the neural signal creates significant challenges, especially against the background of instrumentation noise (on the order of 2-4 µ V root-mean-square (RMS)). Adding considerably to this challenge is the interference from nearby muscles (electromyographic (EMG) activity), which can be an order of magnitude larger than the neural signal when both are recorded using a monopolar reference. The EMG bandwidth (approximately 5-500 Hz) has partial overlap with the neural signal, precluding effective removal using linear filtering without loss of information.

Neural signals may also be directly evoked, or modulated, by mechanical, chemical, or electrical stimulation. When this occurs, many axons produce APs simultaneously, and the resulting neural signal (the evoked compound AP (eCAP)) is the result of the superposition of these APs. In this case the amplitude of the eCAP will be much larger than that of a single AP (∼100 µV for cuffs), see figure 6(a) for an exemplar recording of both spontaneous APs and eCAPs.

2.2. Amplification and acquisition

At the amplification and acquisition stage, a well-chosen reference montage can be used to minimise the contributions of interfering sources. The most widespread example of this approach is the tripolar arrangement, in which the signals at the end electrodes are averaged and used as a [ 33]. This configuration helps suppress EMG interference by taking advantage of the linearisation of these electric fields along the length of the recording array [ 38], and can be implemented through several alternative differential recording arrangements [ 39]. For intraneural recordings both monopolar and bipolar approaches have been reported [ 40, 41]. Examples of the monopolar, bipolar, and tripolar amplifier configurations are given in figure 2.

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Acquisition is generally less critical, and can be performed by most good quality analogue-to-digital converters with sufficient sampling rates (typically >30 kHz), although techniques that measure the conduction velocity of the neural signals may require supra-nyquist sample rates to adequately sample fast APs if the inter-electrode distance is small.

2.3. Signal pre-processing and denoising

Noise and interference in neural recordings can arise from a several sources such as interfering muscle activity [ 38], movement artefacts causing audiophonic noise or triboelectric noise, noise from a high impedance ground, or electromagnetic noise [ 26]. Using the appropriate signal processing techniques can help minimise noise and interference.

At the data pre-processing stage, bandpass filtering is commonly applied to isolate the neural signal, with a high-pass frequency in the 250 Hz-1 kHz range and a low-pass frequency in the 3-7.5 kHz range. If the objective is simply to detect the presence of neural activity, a rectified-bin-integration (RBI) approach can be applied, in which windows that contain neural activity, as well as noise, produce higher values than those that only contain noise [ 42]. This approach, however, is accompanied by a loss of temporal resolution that may preclude the use of many of the techniques described in the following sections.

An additional and more sophisticated pre-processing option is wavelet denoising, which relies on transforming the noisy data into an orthogonal time-frequency domain, thresholding the wavelet coefficients to remove the noise, then transforming back to the original time domain. Selection of the mother wavelet and thresholds are key considerations, but reports have varied about which choices are optimal for neural signals [ 35, 41, 43].

The geometry and configuration of the electrodes used to obtain a recording are critical in determining the type of signals that can be measured. Selecting the most appropriate electrode material, structure, configuration, and geometry for a specific application requires an in-depth understanding of the advantages and disadvantages of each. Ciancio et al [ 44] have summarised these requirements, and from their work and the wider literature it is possible to identify two key characteristics of a PNI.

Selectivity-Selectivity refers to the ability of the PNI to stimulate, or record from, specific axons, fascicles, or nerves, whilst being insensitive to off-target axons, fascicles, or nerves. Depending on the application, the desired stimulation selectivity may differ from the recording selectivity; for example, two different electrodes may be used to stimulate and record in the same prosthesis and the overall selectivity may vary from one application [ 7] to another [ 45].

Stability-Equally important is the stability of the PNI, both for acute and chronic applications. It should be stable over time and should inflict as little physiological or histological damage to the tissue as possible [ 46]. These properties are governed by factors such as the mismatch of mechanical properties between the tissue and the electrode, as well as the immunological reaction of the tissue to different materials and surface treatments [ 47].

3.1. Electrode location

3.1.1. Extraneural electrodes

One exception to this is the microchannel interface, in which the nerve trunk may be micro-dissected into fascicles that may then be placed within channels that constrain the extracellular space [ 49]. This results in fewer axons per channel and typically higher signal amplitudes (∼100 µ V).

3.1.2. Intraneural electrodes

Intraneural electrodes are normally implanted directly inside the fascicles, penetrating the perineurium, and show better selectivity than extraneural electrodes as they have closer contact with the fascicles. Examples of these include thin-film longitudinal intrafascicular electrodes (LIFEs) [ 50], and transverse intrafascicular multi-channel electrodes (TIMEs) [ 51]. In terms of stability, their invasiveness is higher than extraneural electrodes and the implantation itself may cause damage [ 21, 52, 53].

3.1.3. Regenerative interfaces

Regenerative interfaces are often designed to interface with small groups of axons, allowing for selective stimulation and recording with the best possible level of selectivity. Instead of penetrating the nerve with the electrode, the nerve is transected and then supported to regrow through a structure containing electrode channels [ 54]. In one embodiment, the structure resembles a sieve consisting of a piece of material with multiple micropores covered with a conductive material. After transection, each end of the nerve is placed on either side of the sieve and the axons grow through the micropores. A schematic representation of this is shown in figure 2.

It has been shown that neurons will regenerate through the sieve structure, and the sieves are functional as both recording and stimulation devices with high selectivity. However, regenerative electrodes may result in incomplete or constrained regeneration of axons, leading to difficulties with chronic implantation [ 55, 56].

3.1.4. Electrode configuration

Several recent papers review the biocompatibility and stability of different electrode materials and structures [ 31, 57]. However, from the point of view of neural recordings, it is helpful to consider a taxonomy of geometries-as it is the spatial relationship between the signal source and the electrodes, alongside the constraining of the extracellular space, that often defines the recording selectivity and capability.

Electrodes may be grouped to form point measurements (single channel), one-dimensional linear arrays (organised circumferentially, transversely, or longitudinally), or two-dimensional linear arrays. Figure 3 illustrates these geometries using an extraneural cuff interface. The substrate (i.e. insulating tube) of the cuff and FINE interfaces constrains the extracellular space and serves to maintain the spatial relationship between the electrodes. Intraneural electrodes may, or may not, have stable fixation and so while the geometries are applicable to all interfaces, the spatial stability should also be considered.

3.2. Point measurements

One-dimensional point measurements are by far the most common and lend themselves to a wide array of signal processing techniques. Point measurements, in this paper, refer to measurements produced from a single recording channel (i.e. an observation at a single spatial location) that may have been referenced by a monopolar, bipolar, or tripolar configuration.

3.3. One-dimensional arrays

One-dimensional arrays can be formed by placing multiple electrodes either longitudinally, transversely, or circumferentially. Thus, both temporal and spatial classification becomes possible.

Temporal classification-relies on the fact that the propagation of the AP may be observed by placing an array of electrodes located longitudinally along the length of the nerve. Different properties of the APs, such as conduction velocity (which is proportional to axon diameter), may then be used to discriminate APs from individual axons, or types of axons [ 58, 59]. These approaches do not require any prior knowledge about the AP morphology, and many can improve the signal-to-noise ratio (SNR) of the recording by averaging over multiple recording channels.

Spatial classification-relies on the fact that an array of electrodes arranged circumferentially can selectively identify activity from within different fascicles or axons, based on the spatial location of each electrode with respect to the neural source. Passive recording approaches include source localisation and types of beamforming, many of which do not require prior knowledge about the expected morphology of the AP but do require high SNRs to localise activity to individual fascicles [ 60].

3.4. Two-dimensional arrays

Circumferentially and longitudinally spaced electrodes can be combined to form a two-dimensional structure that enables the observation of APs in both space and time [ 61].

Spatiotemporal classification-combines the benefits of longitudinal and circumferential arrays for a more comprehensive and robust characterisation of the APs. Classification can be performed using templates, or by training a convolutional neural network (CNN) to recognise the spatiotemporal patterns associated with specific neural activity [ 22].

Section 3 introduced a taxonomy of electrode geometries and explained how the choice of geometry impacts the available signal processing methods-including temporal, spatial and spatiotemporal. Several key analysis techniques will now be introduced and discussed in the context of content extraction. The information of interest in peripheral nerve recordings can be broken down more broadly into two categories: anatomical and functional.

4.1. Anatomical content

Anatomical content pertains to the size, shape, type, and positions of structures within the nerve. In terms of PNI, anatomical information can be provided by making observations of the propagation direction (afferent versus efferent), axon type (e.g. A

Throughout this paper, direction sensitivity refers to the ability to discriminate afferent versus efferent, velocity refers to the conduction velocity of each AP, and location of neural sources refers to the determination of the spatial location of the source of the neural signal within the nerve. Anatomical content is most readily obtained using extraneural electrodes, as they provide a macro view of the activity within the entire nerve, as opposed to intraneural electrodes that offer a more microscopic (and thus spatially localised) view.

4.2. Functional content

Functional content within a nerve pertains to a specific organ or system. For example, signals within the ulnar nerve may encode sensation (via cutaneous afferents) from the hand and forelimb. The overall presence (e.g. a non-selective power measurement) of neural activity can also be considered functional content. Functional interpretation of a signal can be made without knowledge of the anatomical underpinnings (e.g. where exactly in the nerve an axon is located) but does require knowledge of the nerve's innervation.

A common approach to extracting functional content is to identify single unit activity (i.e. APs that result from specific axons) in a process called spike sorting, wherein individual APs are grouped into clusters based on morphology. The resulting single unit labels (spike trains) can then be used directly in a neuroprosthesis without reference to any anatomical organisation. Spike sorting is most used in intraneural electrode configurations wherein the SNR is high and individual APs can be observed. Figure 4 illustrates this process for exemplar data with three active axons.

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More generally, multi-unit single axon AP trains, eCAPs, or signal windows from either intraneural or extraneural recordings can be associated with a particular function of interest through classification or clustering approaches. In this paper the task of associating neural signals with different functional events is termed discrimination of neural pathways.

In some cases, certain information can overlap anatomical and functional content. For example, the location of neural sources refers to the spatial location of the source within the nerve, but with some a priori information, a functional aspect can be determined. In the popular rat sciatic nerve model, identifying neural sources in the tibial nerve would correspond to dorsiflexion of the ankle while identifying neural activity in the peroneal nerve would correspond to plantarflexion of the ankle.

Deciding what information (anatomical vs functional) to obtain depends largely on the application of interest. If the intended use is focused on understanding the underlying physiology, anatomical content will likely be more applicable. On the other hand, functional content may be of more interest if the intended use is to obtain a control signal (i.e. to use in a neuroprosthetic device).

4.3. Content extraction techniques

Once the information of interest is known, the appropriate electrode configuration and data analysis techniques can be employed. Figure 5 introduces a taxonomy of the different methods and the applicable electrode configurations that can be used to determine anatomical or functional content. For example, if one is interested in imaging (producing a cross sectional image with the location of neural sources identified), then a 1D-circumferential cuff could be employed alongside the electrical impedance tomography (EIT).

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4.3.1. Time series analysis

The most common way of analysing neural signals still lies in using time-domain, frequency-domain, or statistical features without necessarily relying on any aspect of the anatomical information (i.e. fibre velocity, spatial location) directly.

Time domain techniques to smooth the neural signal over a window can be used to detect neural activity [ 62]. The most common approach is the RBI operation, where the signal is rectified, binned into time windows, and integrated. This technique extracts the signal's envelope and enables a simpler and smoother signal for analysis compared to the noisier raw recordings. This signal can then be thresholded to determine if a neural source of interest is active.

Another approach for identifying neural activity is to observe the statistical properties of the signal and noise. For example, the autocorrelation matrix of white noise will be diagonal because the samples are completely uncorrelated. Therefore, the eigen-decomposition of this matrix will yield a single non-zero eigenvalue. In contrast, a recording containing both neural data and noise will have off-diagonal elements in the autocorrelation matrix, and thus more than one eigenvalue. The difference between the greatest and smallest eigenvalue can be used to detect neural signals [ 42].

These techniques are simple and effective for detecting neural activity but alone are often inadequate for obtaining more sophisticated functional or anatomical content. However, if the dominant application is to detect neural activity, these techniques are typically sufficient and can be quickly and easily implemented.

4.3.2. Choice of reference

Reference choice has a significant impact on the recording quality of a PNI as it can attenuate interfering signals and can also be used to determine the direction of propagation (i.e. afferent vs efferent signals) without the need for other techniques or methods.

4.3.3. Template matching

The main advantage of template matching is that it is amenable to implementation on an online system, but its effectiveness is reduced if the templates are similar or the number of distinct sources increases. Another drawback occurs when AP shapes overlap in time [ 68] and thus choosing templates that represent the underlying neural activity may not be straightforward. The use of multi-contact electrodes has helped mitigate the effects of this issue.

4.3.4. Velocity selective recording

The concept of velocity discrimination is founded on the fact that the conduction velocity of an AP is a function of the axon properties, all of which are assumed to be either time invariant (e.g. myelin thickness, diameter, membrane properties) or tightly regulated (e.g. temperature, ionic concentrations) [ 69]. The axon diameter and myelin's presence (or lack thereof) are the main factors that determine the difference in conduction velocity from one axon to another.

The velocity of an AP can be computed by delaying the signals recorded from each element of a longitudinal array relative to one another by an interval that corresponds to the conduction velocity before summing together [ 70, 71]. One advantage of this process is the ability to distinguish afferent and efferent neural activity by simply applying a negative delay.

The first demonstrations of velocity selective recording were made using electrical stimulation to recruit large amplitude eCAPs, in worm [ 71] frog, pig, and rat [ 27, 28, 72- 74]. The delay-and-add process is used to provide a spectral representation of eCAP recordings, where the spectrum is presented with respect to velocity rather than frequency. Figure 6(a) shows a time domain eCAP recording and the corresponding velocity spectrum.

4.3.5. Source localisation and beamforming

Most, if not all, techniques for locating neural sources derive from source localisation and beamforming techniques that are related to inverse problems in electroencephalography [ 75]. Briefly, the inverse problem of bioelectric source localisation is based on the equation:

where d is an M ×1 vector containing the recorded data from M electrode contacts, j is an N × 1 vector whose entries represent the magnitude of the current dipoles distributed in the region under consideration, and L, known as the lead field matrix, is an M × N matrix whose entries represents the influence of a unit current dipole on the potential recorded at a particular electrode. is an M × 1 vector of additive noise.

The objective is to recover j based on the measurements of d and estimate of L.

Beamforming is a signal processing technique that uses spatial filters that combine recordings made at different spatial locations to enhance (via constructive interference) the selectivity of the recordings. When tuned correctly, this can be used to localise neural sources within the nerve. The advantage of this technique is that it can provide both anatomical (the location of the neural activity within the nerve) and, with some a priori information, functional content. However, solving the inverse problem is non-trivial and in small nerves the neural sources of interest are much closer in distance, reducing the ability to localise them.

Most source localisation approaches have been attempted with extraneural electrodes [ 25, 26, 29, 76- 82]. An approach using FINE electrodes has shown the most promising results, demonstrating the feasibility of real-time implementation in chronic recordings involving canines [ 26, 82].

4.3.6. Spatiotemporal signatures

The main advantage of this technique comes from the direct integration of the temporal and spatial information allowing for a more comprehensive characterisation of neural activity. These spatiotemporal signatures have been used as an input to a CNN which demonstrated the ability to discriminate naturally evoked CAPs (nCAPs) and be used to reconstruct firing patterns of different neural pathways [ 22]. This network is known as the extraneural spatiotemporal compound AP extraction network or ESCAPE-NET. The overall ESCAPE framework can be seen in figure 7 alongside an example of a spatiotemporal signature.

This technique has shown promising results in an acute rat model. A recent simulation study [ 84] aimed at mimicking chronic conditions suggested that the selectivity of the spatiotemporal signature can be maintained by establishing a recalibration schedule for the classifier.

4.3.7. Electrical impedance tomography (EIT)

EIT is a method that can provide fascicular-level selectivity with an extraneural approach using electrodes distributed around the nerve. EIT is an emerging medical imaging technique in which changes in the impedance of a conductive volume, such as a nerve, may be imaged using an array of external electrodes [ 85, 86]. In this method, a flexible, cylindrical, multi-electrode cuff is placed around a nerve, and the imaging technique of fast EIT is applied to image the activity within the fascicles. Changes in impedance caused by small decreases in bulk tissue resistance occur as ion channels open and close, and these can be detected (with some averaging) using the external electrodes.

Mathematically, EIT is similar in principle to inverse source localisation. However, EIT has significant advantages including: more independent data (for N electrodes, there are O(N ²) independent measurements at a time compared to O(N) for inverse source localisation), a potentially unique solution, no field cancellation problem, and no theoretical limitations on the accuracy [ 87- 89].

The principle of operation is that small currents (typically 30 µA at 6 kHz) are injected between different pairs of electrodes, while the resulting voltage is measured at every other pair. This process is repeated over all electrodes to produce a set of measurements that can be re-constructed into an image of the nerve. Figure 8 illustrates the principle of fast neural EIT in a model peripheral nerve. An impedance change associated with neural activity occurs in figure 8(b) relative to the baseline cases in figures 8(a) and (c). This change in impedance can be observed as a change in the measured voltage of a pair of electrodes to a current applied in another pair of electrodes in figure 8(d). Multiple measurements of this change can be used to produce an image of the activity in figures 8(e) and (h). EIT is an effective recording approach for eCAPs, wherein the averaging process can readily be performed, but has yet to be demonstrated with spontaneous neural signals.

The signal processing and recording methods presented in this paper are given as examples, and new methods will undoubtedly be developed in due course. However, the taxonomy in figure 5 should provide the reader with a powerful tool for selecting the electrode geometry and thus the type of information that can be extracted by the different methods. Having now covered the fundamentals of neural recording and introduced a range of electrode geometries and signal processing methods, the following section will cover the translational considerations and experimental challenges associated with animal models.

The previous sections have introduced the fundamentals of peripheral nerve recordings, from the biophysics of the APs, through the electrode geometry, and then the signal processing. This section will introduce the animal models typically used to develop and test PNIs and discuss the potential translational issues using the context of the cervical vagus nerve as an example target.

5.1. Experimental models

Most of the development of neural interfaces is performed using animal models, in either acute or chronic experiments. There are both ethical and scientific reasons for using animal models; however, these models also lead to several associated translational challenges. A recent detailed review of animal models used for PNI development can be found in Aman et al [ 91].

5.1.1. Acute experiments

In acute surgical experiments, the electrodes (recording, stimulation, or both) are implanted in anesthetised animals and the target nerve is explored over the course of a few hours whilst the animal remains unconscious [ 17, 25, 72, 83, 92, 93]. The animal is then terminated at the end of the experiment. Acute experiments are beneficial because they provide a stable platform for interrogating the nervous system that includes any and all organs or tissues of interest, whilst removing several of the challenges associated with chronic experiments such as movement artefacts and the need for either percutaneous connectors or a wireless telemetry system [ 38].

However, the anaesthetic regime employed for the surgery has an influence on the electrophysiology that remains unclear, even though the molecular mechanisms of anaesthetics are well characterised. For example, it is known that both isoflurane and ketamine, two widely used anaesthetic paradigms, differentially impact sensory processing in the mouse primary visual cortex [ 94]. The complex interactions between the anaesthetic agents, and their compound effect on the electrophysiology of the animal, remain problematic and may hinder the direct translation of results to awake animals.

5.1.2. Chronic experiments

In chronic experiments the electrodes are implanted under general anaesthesia, but the animals are then recovered for a period of time (from days to months) [ 26, 95- 97]. Recording may take place during the implantation surgery, while the animal is awake and freely moving, or during a termination surgery. The former and the latter have the advantage of not requiring percutaneous connectors or a wireless telemetry system but suffer the same drawbacks as acute experiments. Recordings made whilst the animals are awake and freely moving are, for many applications, the most representative but also by far the most challenging. One advantage of using percutaneous connectors is that measurements of the electrode impedances may be made directly, and then related to the recorded signal amplitudes.

It is important that an appropriate species be chosen for the model, based not just on the ease of the experiment but also on any translational opportunities or issues. If the goal of the experiment is to elucidate some fundamental electrophysiologic property or phenomena, then a well-characterised species such as mice or Xenopus Laevis frogs may be suitable. Likewise, the development of electrode materials and the associated instrumentation may not depend greatly on the species and so mice or rats may be chosen based on reasons of cost or simplicity. However, if the goal-at any point in time-is to translate the research into humans, then the choice of model is critical. The difference between the peripheral nerves of humans and other mammals is not obvious; differences may exist in gross anatomy and geometry of the nerves, the type and level of fascicular structure and vascularisation, and the distribution of myelinated versus unmyelinated axons [ 98, 99].

A good example of the difference between species can be found by considering the cervical vagus nerve. The vagus is the tenth cranial nerve and for many years has been the focus of significant interest as a neuromodulation target for the treatment of diseases as broad-ranging as intractable epilepsy [ 100, 101], diabetes [ 102, 103], and rheumatoid arthritis [ 104- 106]. Figure 9 shows the comparative anatomy of the cervical vagus nerve between mouse, rat, canine, non-human primate (NHP), pig and human [ 90]. Each of these will be discussed in the context of the individual animal model.

5.1.3. Rodents and small animals

Rodents, typically mice or rats, have been the staple model for biomedical research for a long time. They are well characterised, easy to care for, and have the benefit of being available in a multitude of different inbred or outbred strains. From an experimental perspective, they can be anaesthetised and maintained without complex anaesthetic equipment. Indeed in rats and mice it is commonplace to use only a single intraperitoneal administration of an agent such as urethane [ 107]. The peripheral nerves are, of course, physically much smaller than in humans. On the one hand this can make the surgical approach to the nerves relatively straightforward, as there is minimal tissue or bone to remove. On the other hand, it makes the design and placement of electrodes far more difficult, thus limiting the number of electrodes that can be placed as well as the separation between stimulation and recording electrodes. The latter point is important as an increased separation between the stimulation and recordings sites is one method for reducing or eliminating stimulation artefacts from recordings.

Animals such as guinea pigs, cats, and rabbits are an attractive middle ground between rodents and larger animals but are much less explored in the literature on electrophysiology. Cats have seen the greatest attention [ 65, 108, 109]. Aside from potential inexperience in working with these species, there is likely to be a stronger perceived ethical concern associated with species that are companion animals. Interestingly, this has led to the implantation of neuromodulation devices in companion animals as a therapy, for example the Brindley device has been implanted in companion dogs to restore urinary bladder control [ 110].

Turning to the cervical vagus as an example target, it is smaller in mice () than in rats (), and typically consists of only 1-2 fascicles (figure 9(a)). The canine vagus is larger again () and has the thickest epineurium across most models, resulting in a greater distance from either the stimulation or recording electrodes placed on the epineural surface to the axons. This in turn would likely alter the stimulation thresholds and the amplitudes of the recorded neural signals. In the case of mice, rats, and canines, the fascicular structure is far less complex than that in humans-further complicating any translation of design parameters from one to the other.

5.1.4. Large animals

Large animals-in this context sheep and pigs-have long been common-place in neuromodulation research, although their prevalence is in part limited by their comparatively complex requirements for both housing and surgical management. Unlike rodents, sheep and pigs are generally not as well characterised, and are typically obtained from commercial farmers or kept in small groups for breeding purposes. Their housing and husbandry requirements are not necessarily more difficult than those of rodents but do require greater space and nutritional resources. Pigs in particular are frequently reported in experimental research and have been for many decades [ 72, 73, 101, 111].

Compared to rodents, the surgical approaches to the peripheral nerves of pigs and sheep can be more complex and time consuming, although the gross anatomy and the approach, in general, have the potential to be more directly translated to humans. Anaesthetic induction and maintenance is complex, and almost always requires a dedicated anaesthetist and mechanical ventilation providing volatile anaesthetics such as sevoflurane or isoflurane [ 72]. Agents such as fentanyl, propofol, and ketamine may be administered before or during surgery, typically as a bolus, although continuous rate infusion of propofol is possible [ 112]. The impact of these agents on the electrophysiology of the animals is more challenging in part due to the number of agents administered.

Returning to the vagus nerve as an example target, it is of similar size () in pigs, sheep, and humans [ 113]. The number of fascicles in the vagus nerve of pigs and sheep is on the order of 30, whereas in humans it is closer to 10. There is a similar thickness of epineurium and level of vascularisation across the three species. Thus, pigs and sheep represent appropriate models for developing electrodes, instrumentation, and surgical approaches that might be translated into humans. The distance between the axons and electrodes placed on the epineurium is likely to be similar, so stimulation thresholds and recorded signal amplitudes ought to be comparable.

5.1.5. Humans and non-human primates (NHPs)

Experiments involving either humans or NHPs are the least common, due in large part to the ethical and regulatory issues surrounding them. In humans, most experiments are performed as part of a larger rehabilitation package wherein the patient is implanted with a therapeutic device that may also collect data, or as part of a clinical trial for an innovative technology. Good examples of this include PNIs that are used for either controlling upper-limb prosthesis or for proving sensory feedback from a prosthesis-such experimental interfaces have been implanted in humans for several weeks or months [ 6, 7, 114, 115].

NHPs have predominantly been used in experiments that target the CNS, wherein single unit recordings can be made from awake, behaving animals using high-density electrode arrays [ 116]. The surgical approaches used in NHPs are most like those used in humans and other large mammals, an attribute that both aids potential clinical translation but also raises the costs and complexities of experimentation. One significant benefit with most NHPs is that they can be trained to perform purposive motor tasks (such as reaching and grasping), and this has made them popular in various areas of sensorimotor research, for example in the development of PNIs for the control of prosthetic limbs [ 117] or the use of optogenetic modulation to improve motor functionality [ 118]. However, there is often significant training time (and thus cost) associated with the development of purposive experimental paradigms [ 91].

Returning to the vagus nerve as an example target, the vagus nerve in NHPs is smaller than that of the human () with notably less complex fascicular organisation [ 113]. The number of fascicles in the vagus nerve of NHPs is typically only 1-2, but there is a similar (relative) thickness of epineurium and level of vascularisation. Both the marked differences in vagus nerve diameter and fascicular organisation make it challenging to appropriately design a translatable interface for this nerve, although the NHP model can be valuable for the testing of chronic implant stability. From both an economic and ethical standpoint, NHPs should only be used when development of an interface is sufficiently advanced so that the benefits of this model, like complex movement behaviour and similarity to human biomechanics, are scientifically mandatory.

5.2. Chronic recordings

There are several points during a chronic experiment when neural recordings can be made: during implantation surgery, whilst awake and freely moving, and during termination surgery. There is intrinsic value in each of these, and many breakthroughs in the chronic stability of the neural interfaces have been made without recording from an awake animal-see for example the recording of bladder activity in rats [ 49]. However, it is often the goal to record neural signals whilst the subject is awake and freely moving. The challenges associated with doing this are multiple and span from husbandry and care to electrical interference and noise.

Fundamentally, the recording system needs to measure the potentials at the electrodes and communicate this to a remote device for processing or logging. The most straightforward way of achieving this is via percutaneous connectors; implanted cables are routed from the electrodes to the connector, and external instrumentation can then be connected on an ad-hoc basis [ 26]. Challenges include healing of the wound surrounding the connector, risk of damage due to normal or abnormal animal behaviour, and mounting the connector at a suitable anatomic site. Electrical issues can arise from the length of the cable that may be required between the electrodes and the amplifier, or from triboelectric effects from the cables themselves [ 26].

Implanting the instrumentation is an attractive alternative to percutaneous connectors, for example using a telemeter [ 119]. In this scenario, the instrumentation, potentially including a power source and a wireless communication system, is packaged in an implantable device that is located near the recording electrodes. Data storage is likely to be limited, and in most cases a receiving device must be placed on the skin over the site of the telemeter to enable data collection.

Finally, there are fundamental challenges associated with the movement of the animal. Modern amplifiers are available with very high common-mode rejection ratios (CMRRs), and so in theory, should be more than capable of rejecting common-mode signals from nearby muscles. In practice, however, differential electrode impedances and the filter networks that might be placed before the amplifiers, will degrade the CMRR. Thus, in recordings from awake and freely moving animals, it remains very challenging to ensure that recordings are not contaminated by interference from nearby muscles.

6.1. Closed-loop interfaces

Information extracted from peripheral nerve activity can be used to improve functional or health outcomes. This relationship typically takes the form of closed-loop stimulation, where the timing, pattern and/or location of stimulation are continuously adjusted based on the measured neural activity. The translational success of peripheral nerve recording techniques is therefore closely tied to closed-loop neuromodulation systems. Several studies in animal models have demonstrated the control of FES based on limb position or state estimates derived from peripheral nerve recordings, using both extraneural [ 120, 121] and intraneural [ 122] recordings. Early examples in humans included the regulation of grasping strength in a hand neuroprosthesis based on volar digital nerve feedback [ 8], and foot-drop correction using sural nerve recordings [ 123], both using nerve cuff recordings.

More recent work in humans demonstrated decoding of motor intention combined with sensory stimulation, using slanted microelectrode arrays [ 14]. While most of these efforts focused on neuroprosthetic applications, a study by Plachta et al is noteworthy for demonstrating an application related to bioelectronics medicine, namely the control of blood pressure using selective recording and stimulation of the vagus nerve [ 16].

Despite these examples, the number of studies that have demonstrated closed-loop stimulation based on peripheral trunk recordings remains low, although a substantial number of studies cite this concept as their motivation. This gap can be partially attributed to the challenges with obtaining informative and stable peripheral nerve recordings. Another notable challenge is the need for strategies for removing stimulation artefacts to effectively coordinate recording and stimulation. Artefact removal in this context may require a combination of hardware (e.g. blanking systems to prevent amplifier saturation during stimulation) and signal processing components [ 124], which increases the complexity of the instrumentation required and contributes to the low number of studies. Nonetheless, the variety of techniques described in this article and the recent acceleration of the field suggest that we are at a turning point in this regard and artefact rejection signal processing schemes that demonstrate a 25-40 dB rejection in the artefact are now available [ 125].

6.2. Autonomic nerve applications

Most of the techniques described in this manuscript have been developed for sensorimotor applications, such as the control of prosthetic limbs. However, there is a growing interest in the ability to record from smaller fibres within the autonomic nervous system (such fibres are frequently of interest in pain research). Recent advancements in hardware (e.g. more compact electrodes and multiple electrode contacts) have led to opportunities for recording from these small autonomic nerves. Thinly myelinated and un-myelinated axons (e.g. C-fibres) conduct APs more slowly (<2 m s −1) than larger fibres, and the resulting AP and CAP amplitudes are much smaller. Thus, it is challenging to record C-fibre activity without employing a very selective interface (e.g. micro-needles [ 126]) or using supra-maximal stimulation to recruit all of the C-fibres simultaneously and thus maximise the amplitude of the resulting eCAP.

Microneurography, being the easiest way to identify single C-fibre APs, employs a needle that is able to record single or compound APs [ 127]. This approach allows for unambiguous recording and recognition of C-fibre APs. Microneurography is beneficial for developing a better mechanistic understanding of peripheral nerve encoding and modulation but is not suitable in most cases for chronic use.

Novel electrode designs have also been developed for the purposes of recording from small-fibre nerves, but novel signal processing techniques for obtaining small-fibre activity are limited [ 128- 130]. This is mainly because validation of small-fibre activity is extremely challenging due to the low signal amplitude and the slow conduction velocity. Velocity selective recording (VSR) may be of significant benefit in this application as the enhancement of the SNR of the recorded signal will maximise the likelihood of observing APs from small-fibre activity, and both the conduction velocity and the direction can be validated. After locating these small-fibre APs using VSR, template matching and spatiotemporal signatures may be able to separate activities of interest, but no studies using signal processing techniques have shown this possibility.

Interfacing with these slower-conducting nerve fibres will require new or adapted recording and analysis methods. These techniques will need to either enhance the SNRs of the recorded signals to address the low signal amplitudes or be able to isolate small-fibre activity. It is likely that a combination of novel electrode design and signal processing approaches will be needed to overcome these challenges to facilitate the growing interest in recording from the autonomic nervous system.

6.3. Alternative paradigms

Electrical recording remains the dominant modality for recording from the PNS. Whilst neuromodulation via direct electrical stimulation is common (including in clinical applications), few, if any, clinical devices record from peripheral nerves. This is despite a clear need driven by the desire to achieve closed-loop, time-invariant, neural interfaces within the PNS.

There has been some (limited) engagement with active modalities (such as EIT) and with optogenetics [ 131, 132], but conventional electrical recording using either intraneural or extraneural electrodes remains the dominant modality. Many nerve interfaces (such as cuffs) are claimed to be chronically implantable-however, these claims require further verification. Stimulation electrodes need only be placed close to the target nerves, and so a cuff can be placed quite loosely around the nerve. Conversely, recording electrodes need to be placed in direct contact with the epineurium (to maximise recorded signal amplitudes), and so have the potential to cause considerable damage when implanted long-term. There are few [ 26, 48] studies describing the results of chronic implantation of recording electrodes.

At the same time, closed-loop neuromodulation is complicated by the fact that large stimulation artefacts-caused by current flowing from the stimulation electrodes onto the recording electrodes-often saturate the recording amplifiers. Some solutions to this problem exist in the form of either signal processing or electrode balancing, but the problem remains.

A novel approach is to record the magnetic fields produced by the axons rather than the electric field. This would have the benefit of not requiring any direct contact with the nerve, as the magnitude of the observed magnetic field is not related to the contact impedance, instead the location is driven by the SNR required. This concept of magnetoneurography has been demonstrated both ex-vivo and in-vitro. Okawa et al used large arrays of superconducting quantum interference devices to record neural activity transcutaneously from both the brachial plexus and the carpal tunnel area during electrical stimulation [ 133, 134]. In the case of the brachial plexus, it does not appear that any stimulation artefact was present, but in the case of the carpal tunnel area, the authors were forced to use the most distal stimulation site and to employ an artefact rejection tool (common spatial pattern). Barbieri et al performed an in-vitro demonstration of recording from mouse muscle, and also developed a model for the magnetic field contribution from the two axial components in the case of a muscle bundle [ 113]. They recorded using giant-magnetoresistance (GMR) sensors and stimulated with a suction pipette. Despite the proximity of the stimulation electrode and the GMR sensors, they reported no stimulation artefacts.

Interest in recording from the PNS continues to be growing, and as more advanced techniques are developed, it becomes more challenging to know which method to adopt. Thus, this tutorial has introduced a taxonomy of electrode configurations and recording techniques that will support the selection process and can be augmented as new methods are developed. The important aspects of selecting an appropriate animal model, as well the challenges associated with acute and chronic recordings, have been introduced. This tutorial provides a good foundation in peripheral nerve recordings for both the novice and the experienced researcher as the field continues to develop.

No new data were created or analysed in this study.

No funding was provided in support of creating this manuscript.

No new data were created or analysed in this study leading to no ethical protocol requirement.

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Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system

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, , and * Journal of Neural Engineering , Volume 19 , Number 4 Citation Ryan G L Koh DOI 10.1088/1741-2552/ac7d74 et al 2022 J. Neural Eng.

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special type of experiments where animals must learn to integrate relevant sensory signals while ignoring a large number of task-irrelevant covariates.

N. A. Roy, J. H. Bak, T. I. B. Laboratory, A. Akrami, C. D. Brody, and J. W. Pillow, ‘Extracting the dynamics of behavior in sensory decision-making experiments’, Neuron, vol. 109, no. 4, pp. 597–610, 2021.

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Extracting the dynamics of behavior in sensory decision-making experiments

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the phenomenon where it was shown that voluntary actions result in attenuation of the action′s sensory effect with the notorious example  that it is difficult to tickle oneself. Sensory attenuation has been thought to result from efferent motor signals predicting the sensory consequences of the upcoming action. Predicted effect and actual sensory feedback are compared: if they correspond, the reafferences that have been anticipated are “canceled“.

C. Roussel, G. Hughes, and F. Waszak, ‘A preactivation account of sensory attenuation’, Neuropsychologia, vol. 51, no. 5, pp. 922–929, 2013.

https://www.sciencedirect.com/science/article/abs/pii/S002839321300047X?casa_token=X6GegYlLFrkAAAAA:DJHUH3W-CJXgs9yEVtwZHd5_l5Se_trJvQ0CD2BwRPJkH6m-u_4wuZfFPTzmSePX19OFe3p-IEE

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Author links open overlay panelCedric Roussel a b, Gethin Hughes a b, Florian Waszak a b

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the approach in processing of electroneurographical (ENG) signal that aims to simply detect the presence of neural activity. It relies on an assumption that windows that contain neural activity, as well as noise, produce higher values than those that only contain noise. The methods results in a decreased temporal resolution.

R. G. L. Koh, J. Zariffa, L. Jabban, S.-C. Yen, N. Donaldson, and B. W. Metcalfe, ‘Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system’, Journal of Neural Engineering, 2022.

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Journal of Neural Engineering , Volume 19 , Number 4 Citation Ryan G L Koh DOI 10.1088/1741-2552/ac7d74 et al 2022 J. Neural Eng. 19 042001

Author affiliations

¹ KITE, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, M5G 2A2, Canada

² Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada

³ Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M4G 3V9, Canada

⁴ Rehabilitation Sciences Institute, University of Toronto, Toronto, ON M5S 2E4, Canada

⁵ Centre for Biosensors, Bioelectronics, and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom

⁶ Innovation and Design Programme, Faculty of Engineering, National University of Singapore, Singapore, Singapore

⁷ The N.1 Institute for Health, National University of Singapore, Singapore, Singapore

⁸ Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6DH, United Kingdom

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⁹ Author to whom any correspondence should be addressed.

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1741-2552/19/4/042001

Abstract

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of PNIs, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

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Neural control is at the heart of our agency in the world. Through a combination of conscious and automatic processes, the nervous system enables us to regulate ourselves and our interactions with our environment. As we seek to modulate, restore, or enhance these abilities, we have turned in recent decades to technologies that can interface effectively with the nervous system. Certain applications benefit from interfaces at the level of the central nervous system (CNS), for example, when dealing with brain disorders such as Parkinson's, psychiatric conditions [ 1, 2] or attempting to coordinate patterns of movement through spinal circuitry [ 3, 4]. In many cases, however, the peripheral nervous system (PNS) offers the advantage of carrying afferent or efferent information that most directly relates to the function of the target of interest.

For most of its history, this field has focused on restoring function lost as a result of amputation [ 5- 7] or paralysis [ 8, 9]. More recently, the scope has broadened considerably (as reviewed in [ 10]), including significant interest in chronic disease applications involving the autonomic nervous system [ 11- 13]. These developments have gone hand-in-hand with new progress in neural interfacing technologies and have spawned the related field of bioelectronic medicine.

Peripheral nerve interfaces (PNIs) can modulate neural activity through stimulation and/or monitor neural control and feedback through recording. Key potential applications of peripheral nerve recordings include the control of prosthetic limbs through extracted motor commands [ 14], the closed-loop control of functional electrical stimulation (FES) systems through extracted afferent information [ 15], and the neuromodulation of body functions through the identification of autonomic control signal [ 16] and electrical disease biomarkers [ 17].

Achieving stable and functional recording in the PNS has been a persistent challenge in neural engineering for several reasons-many of which are distinct from those faced by CNS interfaces. First, in peripheral nerves, the nerve fibres (axons) are tightly packed into fascicles, which are in turn held together by connective tissue to form the nerve trunk [ 18]. The whole structure, in many cases, has a diameter on the order of hundreds of microns to a few millimetres. In this configuration, the axons are densely organised into a relatively small-diameter structure, making it difficult to isolate activity related to a particular function. Second, sparse firing patterns and the lack of large, synchronised populations result in small-amplitude signals. Third, many peripheral nerves experience significant movement during physical activities (e.g. during respiration in larger mammals), and are often located close to muscles whose bioelectrical activity creates substantial interference. Lastly, in the case of chronic implantation, encapsulation tissue can form in and around an electrode and alter the nerve-electrode interface and thus the amplitude and nature of the recorded signals [ 19].

Several device designs have been proposed to create reliable PNIs and these are divided into intraneural and extraneural. The former involves penetration of the device into the nerve trunk, while the latter relies on devices positioned on or near the surface of the trunk [ 20, 21]. These approaches have been considered in a trade-off between selectivity and invasiveness.

Regardless of the approach, multi-channel PNI designs are increasingly providing new possibilities to extract detailed information about the neural function. The availability of multiple channels provides signal processing opportunities for resolving ambiguities that cannot be dealt with in a single channel, and enables powerful machine learning or regression approaches [ 14, 22- 29]. Several recent reviews have covered peripheral nerve electrode designs [ 20, 21, 30, 31], but reviews of the data analysis techniques necessary for the creation of effective PNIs are limited [ 32].

The objective of this tutorial article is to provide a resource that captures the key methods for the analysis of peripheral nerve recordings, and specifically examines the critical interplay between the type of electrode used, the signal analysis techniques applied, and the nature of the information extracted. Further guidance is also given on the importance of appropriate experimental models and the challenges associated with chronic implantation.

The structure of this paper is as follows. Section 2 will introduce general principles and typical approaches to making recordings. Section 3 will introduce the most common electrode geometries and discuss their impact on the nature of information extracted, section 4 will describe the most common signal processing approaches, section 5 will discuss experimental and translational considerations, and section 6 will identify future trends and technologies.

It is important to understand the general principles of a PNI recording system before considering the relative merits afforded by different configurations. The following sections will briefly introduce the properties of peripheral nerve recordings as well as general principles in acquiring and pre-processing these signals.

Figure 1 shows a simplified overview of a typical PNI. Electrodes are placed near, on, or in, the nerve trunk and are connected to low-noise amplifiers (LNAs) and a digital signal processing (DSP) system. The neural signals occurring within the nerve are thus amplified, digitised, and processed to provide an output that can take several forms including: spike trains, continuous time series, and images showing the source of the neural activity within the nerve trunk. This information can then be used to inform a prosthesis such as an artificial limb or a neuromodulation device. It is important first to have an understanding of the nature of neural signals, and so a brief overview will now be given.

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2.1. Biophysics of neural signals

Individual axons produce action currents with magnitudes in the pico-ampere range, due to APs, which may be detected as neural signals. These small action currents give rise to small potentials (on the order of 1 µ V), that are difficult to detect in the presence of noise and interference. There are two ways to obtain a detectable potential: an electrode must be tiny and in close proximity to the axon, so that the potential is produced across the spreading resistance from a node of Ranvier, or there must be a restricted extra-cellular space that creates a high resistance through which the small action currents flow. These two cases give rise to a taxonomy wherein an interface may be defined as operating with either unrestricted (e.g. intraneural) or restricted (e.g. extraneural) extracellular space.

Neural signals that occur spontaneously (i.e. without external stimulation or modulation) are composed of individual APs resulting from normal biological functions. Typically, neurons within different fascicles innervate unrelated tissue, and do not fire synchronously. Consequently, the observed neural signal is characterised by low amplitude and high frequency activity.

Spontaneous signals recorded extraneurally (i.e. within a constrained extracellular space) from the surface of the nerve trunk are rarely larger than 30 µV peak-to-peak [ 33], whereas intraneural signals recorded from inside the trunk can be over 100 µV peak-to-peak [ 34]. Most of the signal power is concentrated in the range of 300 Hz-5 kHz, with the peak below 3 kHz [ 35- 37].

The small amplitude of the neural signal creates significant challenges, especially against the background of instrumentation noise (on the order of 2-4 µ V root-mean-square (RMS)). Adding considerably to this challenge is the interference from nearby muscles (electromyographic (EMG) activity), which can be an order of magnitude larger than the neural signal when both are recorded using a monopolar reference. The EMG bandwidth (approximately 5-500 Hz) has partial overlap with the neural signal, precluding effective removal using linear filtering without loss of information.

Neural signals may also be directly evoked, or modulated, by mechanical, chemical, or electrical stimulation. When this occurs, many axons produce APs simultaneously, and the resulting neural signal (the evoked compound AP (eCAP)) is the result of the superposition of these APs. In this case the amplitude of the eCAP will be much larger than that of a single AP (∼100 µV for cuffs), see figure 6(a) for an exemplar recording of both spontaneous APs and eCAPs.

2.2. Amplification and acquisition

At the amplification and acquisition stage, a well-chosen reference montage can be used to minimise the contributions of interfering sources. The most widespread example of this approach is the tripolar arrangement, in which the signals at the end electrodes are averaged and used as a [ 33]. This configuration helps suppress EMG interference by taking advantage of the linearisation of these electric fields along the length of the recording array [ 38], and can be implemented through several alternative differential recording arrangements [ 39]. For intraneural recordings both monopolar and bipolar approaches have been reported [ 40, 41]. Examples of the monopolar, bipolar, and tripolar amplifier configurations are given in figure 2.

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Acquisition is generally less critical, and can be performed by most good quality analogue-to-digital converters with sufficient sampling rates (typically >30 kHz), although techniques that measure the conduction velocity of the neural signals may require supra-nyquist sample rates to adequately sample fast APs if the inter-electrode distance is small.

2.3. Signal pre-processing and denoising

Noise and interference in neural recordings can arise from a several sources such as interfering muscle activity [ 38], movement artefacts causing audiophonic noise or triboelectric noise, noise from a high impedance ground, or electromagnetic noise [ 26]. Using the appropriate signal processing techniques can help minimise noise and interference.

At the data pre-processing stage, bandpass filtering is commonly applied to isolate the neural signal, with a high-pass frequency in the 250 Hz-1 kHz range and a low-pass frequency in the 3-7.5 kHz range. If the objective is simply to detect the presence of neural activity, a rectified-bin-integration (RBI) approach can be applied, in which windows that contain neural activity, as well as noise, produce higher values than those that only contain noise [ 42]. This approach, however, is accompanied by a loss of temporal resolution that may preclude the use of many of the techniques described in the following sections.

An additional and more sophisticated pre-processing option is wavelet denoising, which relies on transforming the noisy data into an orthogonal time-frequency domain, thresholding the wavelet coefficients to remove the noise, then transforming back to the original time domain. Selection of the mother wavelet and thresholds are key considerations, but reports have varied about which choices are optimal for neural signals [ 35, 41, 43].

The geometry and configuration of the electrodes used to obtain a recording are critical in determining the type of signals that can be measured. Selecting the most appropriate electrode material, structure, configuration, and geometry for a specific application requires an in-depth understanding of the advantages and disadvantages of each. Ciancio et al [ 44] have summarised these requirements, and from their work and the wider literature it is possible to identify two key characteristics of a PNI.

Selectivity-Selectivity refers to the ability of the PNI to stimulate, or record from, specific axons, fascicles, or nerves, whilst being insensitive to off-target axons, fascicles, or nerves. Depending on the application, the desired stimulation selectivity may differ from the recording selectivity; for example, two different electrodes may be used to stimulate and record in the same prosthesis and the overall selectivity may vary from one application [ 7] to another [ 45].

Stability-Equally important is the stability of the PNI, both for acute and chronic applications. It should be stable over time and should inflict as little physiological or histological damage to the tissue as possible [ 46]. These properties are governed by factors such as the mismatch of mechanical properties between the tissue and the electrode, as well as the immunological reaction of the tissue to different materials and surface treatments [ 47].

3.1. Electrode location

3.1.1. Extraneural electrodes

One exception to this is the microchannel interface, in which the nerve trunk may be micro-dissected into fascicles that may then be placed within channels that constrain the extracellular space [ 49]. This results in fewer axons per channel and typically higher signal amplitudes (∼100 µ V).

3.1.2. Intraneural electrodes

Intraneural electrodes are normally implanted directly inside the fascicles, penetrating the perineurium, and show better selectivity than extraneural electrodes as they have closer contact with the fascicles. Examples of these include thin-film longitudinal intrafascicular electrodes (LIFEs) [ 50], and transverse intrafascicular multi-channel electrodes (TIMEs) [ 51]. In terms of stability, their invasiveness is higher than extraneural electrodes and the implantation itself may cause damage [ 21, 52, 53].

3.1.3. Regenerative interfaces

Regenerative interfaces are often designed to interface with small groups of axons, allowing for selective stimulation and recording with the best possible level of selectivity. Instead of penetrating the nerve with the electrode, the nerve is transected and then supported to regrow through a structure containing electrode channels [ 54]. In one embodiment, the structure resembles a sieve consisting of a piece of material with multiple micropores covered with a conductive material. After transection, each end of the nerve is placed on either side of the sieve and the axons grow through the micropores. A schematic representation of this is shown in figure 2.

It has been shown that neurons will regenerate through the sieve structure, and the sieves are functional as both recording and stimulation devices with high selectivity. However, regenerative electrodes may result in incomplete or constrained regeneration of axons, leading to difficulties with chronic implantation [ 55, 56].

3.1.4. Electrode configuration

Several recent papers review the biocompatibility and stability of different electrode materials and structures [ 31, 57]. However, from the point of view of neural recordings, it is helpful to consider a taxonomy of geometries-as it is the spatial relationship between the signal source and the electrodes, alongside the constraining of the extracellular space, that often defines the recording selectivity and capability.

Electrodes may be grouped to form point measurements (single channel), one-dimensional linear arrays (organised circumferentially, transversely, or longitudinally), or two-dimensional linear arrays. Figure 3 illustrates these geometries using an extraneural cuff interface. The substrate (i.e. insulating tube) of the cuff and FINE interfaces constrains the extracellular space and serves to maintain the spatial relationship between the electrodes. Intraneural electrodes may, or may not, have stable fixation and so while the geometries are applicable to all interfaces, the spatial stability should also be considered.

3.2. Point measurements

One-dimensional point measurements are by far the most common and lend themselves to a wide array of signal processing techniques. Point measurements, in this paper, refer to measurements produced from a single recording channel (i.e. an observation at a single spatial location) that may have been referenced by a monopolar, bipolar, or tripolar configuration.

3.3. One-dimensional arrays

One-dimensional arrays can be formed by placing multiple electrodes either longitudinally, transversely, or circumferentially. Thus, both temporal and spatial classification becomes possible.

Temporal classification-relies on the fact that the propagation of the AP may be observed by placing an array of electrodes located longitudinally along the length of the nerve. Different properties of the APs, such as conduction velocity (which is proportional to axon diameter), may then be used to discriminate APs from individual axons, or types of axons [ 58, 59]. These approaches do not require any prior knowledge about the AP morphology, and many can improve the signal-to-noise ratio (SNR) of the recording by averaging over multiple recording channels.

Spatial classification-relies on the fact that an array of electrodes arranged circumferentially can selectively identify activity from within different fascicles or axons, based on the spatial location of each electrode with respect to the neural source. Passive recording approaches include source localisation and types of beamforming, many of which do not require prior knowledge about the expected morphology of the AP but do require high SNRs to localise activity to individual fascicles [ 60].

3.4. Two-dimensional arrays

Circumferentially and longitudinally spaced electrodes can be combined to form a two-dimensional structure that enables the observation of APs in both space and time [ 61].

Spatiotemporal classification-combines the benefits of longitudinal and circumferential arrays for a more comprehensive and robust characterisation of the APs. Classification can be performed using templates, or by training a convolutional neural network (CNN) to recognise the spatiotemporal patterns associated with specific neural activity [ 22].

Section 3 introduced a taxonomy of electrode geometries and explained how the choice of geometry impacts the available signal processing methods-including temporal, spatial and spatiotemporal. Several key analysis techniques will now be introduced and discussed in the context of content extraction. The information of interest in peripheral nerve recordings can be broken down more broadly into two categories: anatomical and functional.

4.1. Anatomical content

Anatomical content pertains to the size, shape, type, and positions of structures within the nerve. In terms of PNI, anatomical information can be provided by making observations of the propagation direction (afferent versus efferent), axon type (e.g. A

Throughout this paper, direction sensitivity refers to the ability to discriminate afferent versus efferent, velocity refers to the conduction velocity of each AP, and location of neural sources refers to the determination of the spatial location of the source of the neural signal within the nerve. Anatomical content is most readily obtained using extraneural electrodes, as they provide a macro view of the activity within the entire nerve, as opposed to intraneural electrodes that offer a more microscopic (and thus spatially localised) view.

4.2. Functional content

Functional content within a nerve pertains to a specific organ or system. For example, signals within the ulnar nerve may encode sensation (via cutaneous afferents) from the hand and forelimb. The overall presence (e.g. a non-selective power measurement) of neural activity can also be considered functional content. Functional interpretation of a signal can be made without knowledge of the anatomical underpinnings (e.g. where exactly in the nerve an axon is located) but does require knowledge of the nerve's innervation.

A common approach to extracting functional content is to identify single unit activity (i.e. APs that result from specific axons) in a process called spike sorting, wherein individual APs are grouped into clusters based on morphology. The resulting single unit labels (spike trains) can then be used directly in a neuroprosthesis without reference to any anatomical organisation. Spike sorting is most used in intraneural electrode configurations wherein the SNR is high and individual APs can be observed. Figure 4 illustrates this process for exemplar data with three active axons.

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More generally, multi-unit single axon AP trains, eCAPs, or signal windows from either intraneural or extraneural recordings can be associated with a particular function of interest through classification or clustering approaches. In this paper the task of associating neural signals with different functional events is termed discrimination of neural pathways.

In some cases, certain information can overlap anatomical and functional content. For example, the location of neural sources refers to the spatial location of the source within the nerve, but with some a priori information, a functional aspect can be determined. In the popular rat sciatic nerve model, identifying neural sources in the tibial nerve would correspond to dorsiflexion of the ankle while identifying neural activity in the peroneal nerve would correspond to plantarflexion of the ankle.

Deciding what information (anatomical vs functional) to obtain depends largely on the application of interest. If the intended use is focused on understanding the underlying physiology, anatomical content will likely be more applicable. On the other hand, functional content may be of more interest if the intended use is to obtain a control signal (i.e. to use in a neuroprosthetic device).

4.3. Content extraction techniques

Once the information of interest is known, the appropriate electrode configuration and data analysis techniques can be employed. Figure 5 introduces a taxonomy of the different methods and the applicable electrode configurations that can be used to determine anatomical or functional content. For example, if one is interested in imaging (producing a cross sectional image with the location of neural sources identified), then a 1D-circumferential cuff could be employed alongside the electrical impedance tomography (EIT).

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4.3.1. Time series analysis

The most common way of analysing neural signals still lies in using time-domain, frequency-domain, or statistical features without necessarily relying on any aspect of the anatomical information (i.e. fibre velocity, spatial location) directly.

Time domain techniques to smooth the neural signal over a window can be used to detect neural activity [ 62]. The most common approach is the RBI operation, where the signal is rectified, binned into time windows, and integrated. This technique extracts the signal's envelope and enables a simpler and smoother signal for analysis compared to the noisier raw recordings. This signal can then be thresholded to determine if a neural source of interest is active.

Another approach for identifying neural activity is to observe the statistical properties of the signal and noise. For example, the autocorrelation matrix of white noise will be diagonal because the samples are completely uncorrelated. Therefore, the eigen-decomposition of this matrix will yield a single non-zero eigenvalue. In contrast, a recording containing both neural data and noise will have off-diagonal elements in the autocorrelation matrix, and thus more than one eigenvalue. The difference between the greatest and smallest eigenvalue can be used to detect neural signals [ 42].

These techniques are simple and effective for detecting neural activity but alone are often inadequate for obtaining more sophisticated functional or anatomical content. However, if the dominant application is to detect neural activity, these techniques are typically sufficient and can be quickly and easily implemented.

4.3.2. Choice of reference

Reference choice has a significant impact on the recording quality of a PNI as it can attenuate interfering signals and can also be used to determine the direction of propagation (i.e. afferent vs efferent signals) without the need for other techniques or methods.

4.3.3. Template matching

The main advantage of template matching is that it is amenable to implementation on an online system, but its effectiveness is reduced if the templates are similar or the number of distinct sources increases. Another drawback occurs when AP shapes overlap in time [ 68] and thus choosing templates that represent the underlying neural activity may not be straightforward. The use of multi-contact electrodes has helped mitigate the effects of this issue.

4.3.4. Velocity selective recording

The concept of velocity discrimination is founded on the fact that the conduction velocity of an AP is a function of the axon properties, all of which are assumed to be either time invariant (e.g. myelin thickness, diameter, membrane properties) or tightly regulated (e.g. temperature, ionic concentrations) [ 69]. The axon diameter and myelin's presence (or lack thereof) are the main factors that determine the difference in conduction velocity from one axon to another.

The velocity of an AP can be computed by delaying the signals recorded from each element of a longitudinal array relative to one another by an interval that corresponds to the conduction velocity before summing together [ 70, 71]. One advantage of this process is the ability to distinguish afferent and efferent neural activity by simply applying a negative delay.

The first demonstrations of velocity selective recording were made using electrical stimulation to recruit large amplitude eCAPs, in worm [ 71] frog, pig, and rat [ 27, 28, 72- 74]. The delay-and-add process is used to provide a spectral representation of eCAP recordings, where the spectrum is presented with respect to velocity rather than frequency. Figure 6(a) shows a time domain eCAP recording and the corresponding velocity spectrum.

4.3.5. Source localisation and beamforming

Most, if not all, techniques for locating neural sources derive from source localisation and beamforming techniques that are related to inverse problems in electroencephalography [ 75]. Briefly, the inverse problem of bioelectric source localisation is based on the equation:

where d is an M ×1 vector containing the recorded data from M electrode contacts, j is an N × 1 vector whose entries represent the magnitude of the current dipoles distributed in the region under consideration, and L, known as the lead field matrix, is an M × N matrix whose entries represents the influence of a unit current dipole on the potential recorded at a particular electrode. is an M × 1 vector of additive noise.

The objective is to recover j based on the measurements of d and estimate of L.

Beamforming is a signal processing technique that uses spatial filters that combine recordings made at different spatial locations to enhance (via constructive interference) the selectivity of the recordings. When tuned correctly, this can be used to localise neural sources within the nerve. The advantage of this technique is that it can provide both anatomical (the location of the neural activity within the nerve) and, with some a priori information, functional content. However, solving the inverse problem is non-trivial and in small nerves the neural sources of interest are much closer in distance, reducing the ability to localise them.

Most source localisation approaches have been attempted with extraneural electrodes [ 25, 26, 29, 76- 82]. An approach using FINE electrodes has shown the most promising results, demonstrating the feasibility of real-time implementation in chronic recordings involving canines [ 26, 82].

4.3.6. Spatiotemporal signatures

The main advantage of this technique comes from the direct integration of the temporal and spatial information allowing for a more comprehensive characterisation of neural activity. These spatiotemporal signatures have been used as an input to a CNN which demonstrated the ability to discriminate naturally evoked CAPs (nCAPs) and be used to reconstruct firing patterns of different neural pathways [ 22]. This network is known as the extraneural spatiotemporal compound AP extraction network or ESCAPE-NET. The overall ESCAPE framework can be seen in figure 7 alongside an example of a spatiotemporal signature.

This technique has shown promising results in an acute rat model. A recent simulation study [ 84] aimed at mimicking chronic conditions suggested that the selectivity of the spatiotemporal signature can be maintained by establishing a recalibration schedule for the classifier.

4.3.7. Electrical impedance tomography (EIT)

EIT is a method that can provide fascicular-level selectivity with an extraneural approach using electrodes distributed around the nerve. EIT is an emerging medical imaging technique in which changes in the impedance of a conductive volume, such as a nerve, may be imaged using an array of external electrodes [ 85, 86]. In this method, a flexible, cylindrical, multi-electrode cuff is placed around a nerve, and the imaging technique of fast EIT is applied to image the activity within the fascicles. Changes in impedance caused by small decreases in bulk tissue resistance occur as ion channels open and close, and these can be detected (with some averaging) using the external electrodes.

Mathematically, EIT is similar in principle to inverse source localisation. However, EIT has significant advantages including: more independent data (for N electrodes, there are O(N ²) independent measurements at a time compared to O(N) for inverse source localisation), a potentially unique solution, no field cancellation problem, and no theoretical limitations on the accuracy [ 87- 89].

The principle of operation is that small currents (typically 30 µA at 6 kHz) are injected between different pairs of electrodes, while the resulting voltage is measured at every other pair. This process is repeated over all electrodes to produce a set of measurements that can be re-constructed into an image of the nerve. Figure 8 illustrates the principle of fast neural EIT in a model peripheral nerve. An impedance change associated with neural activity occurs in figure 8(b) relative to the baseline cases in figures 8(a) and (c). This change in impedance can be observed as a change in the measured voltage of a pair of electrodes to a current applied in another pair of electrodes in figure 8(d). Multiple measurements of this change can be used to produce an image of the activity in figures 8(e) and (h). EIT is an effective recording approach for eCAPs, wherein the averaging process can readily be performed, but has yet to be demonstrated with spontaneous neural signals.

The signal processing and recording methods presented in this paper are given as examples, and new methods will undoubtedly be developed in due course. However, the taxonomy in figure 5 should provide the reader with a powerful tool for selecting the electrode geometry and thus the type of information that can be extracted by the different methods. Having now covered the fundamentals of neural recording and introduced a range of electrode geometries and signal processing methods, the following section will cover the translational considerations and experimental challenges associated with animal models.

The previous sections have introduced the fundamentals of peripheral nerve recordings, from the biophysics of the APs, through the electrode geometry, and then the signal processing. This section will introduce the animal models typically used to develop and test PNIs and discuss the potential translational issues using the context of the cervical vagus nerve as an example target.

5.1. Experimental models

Most of the development of neural interfaces is performed using animal models, in either acute or chronic experiments. There are both ethical and scientific reasons for using animal models; however, these models also lead to several associated translational challenges. A recent detailed review of animal models used for PNI development can be found in Aman et al [ 91].

5.1.1. Acute experiments

In acute surgical experiments, the electrodes (recording, stimulation, or both) are implanted in anesthetised animals and the target nerve is explored over the course of a few hours whilst the animal remains unconscious [ 17, 25, 72, 83, 92, 93]. The animal is then terminated at the end of the experiment. Acute experiments are beneficial because they provide a stable platform for interrogating the nervous system that includes any and all organs or tissues of interest, whilst removing several of the challenges associated with chronic experiments such as movement artefacts and the need for either percutaneous connectors or a wireless telemetry system [ 38].

However, the anaesthetic regime employed for the surgery has an influence on the electrophysiology that remains unclear, even though the molecular mechanisms of anaesthetics are well characterised. For example, it is known that both isoflurane and ketamine, two widely used anaesthetic paradigms, differentially impact sensory processing in the mouse primary visual cortex [ 94]. The complex interactions between the anaesthetic agents, and their compound effect on the electrophysiology of the animal, remain problematic and may hinder the direct translation of results to awake animals.

5.1.2. Chronic experiments

In chronic experiments the electrodes are implanted under general anaesthesia, but the animals are then recovered for a period of time (from days to months) [ 26, 95- 97]. Recording may take place during the implantation surgery, while the animal is awake and freely moving, or during a termination surgery. The former and the latter have the advantage of not requiring percutaneous connectors or a wireless telemetry system but suffer the same drawbacks as acute experiments. Recordings made whilst the animals are awake and freely moving are, for many applications, the most representative but also by far the most challenging. One advantage of using percutaneous connectors is that measurements of the electrode impedances may be made directly, and then related to the recorded signal amplitudes.

It is important that an appropriate species be chosen for the model, based not just on the ease of the experiment but also on any translational opportunities or issues. If the goal of the experiment is to elucidate some fundamental electrophysiologic property or phenomena, then a well-characterised species such as mice or Xenopus Laevis frogs may be suitable. Likewise, the development of electrode materials and the associated instrumentation may not depend greatly on the species and so mice or rats may be chosen based on reasons of cost or simplicity. However, if the goal-at any point in time-is to translate the research into humans, then the choice of model is critical. The difference between the peripheral nerves of humans and other mammals is not obvious; differences may exist in gross anatomy and geometry of the nerves, the type and level of fascicular structure and vascularisation, and the distribution of myelinated versus unmyelinated axons [ 98, 99].

A good example of the difference between species can be found by considering the cervical vagus nerve. The vagus is the tenth cranial nerve and for many years has been the focus of significant interest as a neuromodulation target for the treatment of diseases as broad-ranging as intractable epilepsy [ 100, 101], diabetes [ 102, 103], and rheumatoid arthritis [ 104- 106]. Figure 9 shows the comparative anatomy of the cervical vagus nerve between mouse, rat, canine, non-human primate (NHP), pig and human [ 90]. Each of these will be discussed in the context of the individual animal model.

5.1.3. Rodents and small animals

Rodents, typically mice or rats, have been the staple model for biomedical research for a long time. They are well characterised, easy to care for, and have the benefit of being available in a multitude of different inbred or outbred strains. From an experimental perspective, they can be anaesthetised and maintained without complex anaesthetic equipment. Indeed in rats and mice it is commonplace to use only a single intraperitoneal administration of an agent such as urethane [ 107]. The peripheral nerves are, of course, physically much smaller than in humans. On the one hand this can make the surgical approach to the nerves relatively straightforward, as there is minimal tissue or bone to remove. On the other hand, it makes the design and placement of electrodes far more difficult, thus limiting the number of electrodes that can be placed as well as the separation between stimulation and recording electrodes. The latter point is important as an increased separation between the stimulation and recordings sites is one method for reducing or eliminating stimulation artefacts from recordings.

Animals such as guinea pigs, cats, and rabbits are an attractive middle ground between rodents and larger animals but are much less explored in the literature on electrophysiology. Cats have seen the greatest attention [ 65, 108, 109]. Aside from potential inexperience in working with these species, there is likely to be a stronger perceived ethical concern associated with species that are companion animals. Interestingly, this has led to the implantation of neuromodulation devices in companion animals as a therapy, for example the Brindley device has been implanted in companion dogs to restore urinary bladder control [ 110].

Turning to the cervical vagus as an example target, it is smaller in mice () than in rats (), and typically consists of only 1-2 fascicles (figure 9(a)). The canine vagus is larger again () and has the thickest epineurium across most models, resulting in a greater distance from either the stimulation or recording electrodes placed on the epineural surface to the axons. This in turn would likely alter the stimulation thresholds and the amplitudes of the recorded neural signals. In the case of mice, rats, and canines, the fascicular structure is far less complex than that in humans-further complicating any translation of design parameters from one to the other.

5.1.4. Large animals

Large animals-in this context sheep and pigs-have long been common-place in neuromodulation research, although their prevalence is in part limited by their comparatively complex requirements for both housing and surgical management. Unlike rodents, sheep and pigs are generally not as well characterised, and are typically obtained from commercial farmers or kept in small groups for breeding purposes. Their housing and husbandry requirements are not necessarily more difficult than those of rodents but do require greater space and nutritional resources. Pigs in particular are frequently reported in experimental research and have been for many decades [ 72, 73, 101, 111].

Compared to rodents, the surgical approaches to the peripheral nerves of pigs and sheep can be more complex and time consuming, although the gross anatomy and the approach, in general, have the potential to be more directly translated to humans. Anaesthetic induction and maintenance is complex, and almost always requires a dedicated anaesthetist and mechanical ventilation providing volatile anaesthetics such as sevoflurane or isoflurane [ 72]. Agents such as fentanyl, propofol, and ketamine may be administered before or during surgery, typically as a bolus, although continuous rate infusion of propofol is possible [ 112]. The impact of these agents on the electrophysiology of the animals is more challenging in part due to the number of agents administered.

Returning to the vagus nerve as an example target, it is of similar size () in pigs, sheep, and humans [ 113]. The number of fascicles in the vagus nerve of pigs and sheep is on the order of 30, whereas in humans it is closer to 10. There is a similar thickness of epineurium and level of vascularisation across the three species. Thus, pigs and sheep represent appropriate models for developing electrodes, instrumentation, and surgical approaches that might be translated into humans. The distance between the axons and electrodes placed on the epineurium is likely to be similar, so stimulation thresholds and recorded signal amplitudes ought to be comparable.

5.1.5. Humans and non-human primates (NHPs)

Experiments involving either humans or NHPs are the least common, due in large part to the ethical and regulatory issues surrounding them. In humans, most experiments are performed as part of a larger rehabilitation package wherein the patient is implanted with a therapeutic device that may also collect data, or as part of a clinical trial for an innovative technology. Good examples of this include PNIs that are used for either controlling upper-limb prosthesis or for proving sensory feedback from a prosthesis-such experimental interfaces have been implanted in humans for several weeks or months [ 6, 7, 114, 115].

NHPs have predominantly been used in experiments that target the CNS, wherein single unit recordings can be made from awake, behaving animals using high-density electrode arrays [ 116]. The surgical approaches used in NHPs are most like those used in humans and other large mammals, an attribute that both aids potential clinical translation but also raises the costs and complexities of experimentation. One significant benefit with most NHPs is that they can be trained to perform purposive motor tasks (such as reaching and grasping), and this has made them popular in various areas of sensorimotor research, for example in the development of PNIs for the control of prosthetic limbs [ 117] or the use of optogenetic modulation to improve motor functionality [ 118]. However, there is often significant training time (and thus cost) associated with the development of purposive experimental paradigms [ 91].

Returning to the vagus nerve as an example target, the vagus nerve in NHPs is smaller than that of the human () with notably less complex fascicular organisation [ 113]. The number of fascicles in the vagus nerve of NHPs is typically only 1-2, but there is a similar (relative) thickness of epineurium and level of vascularisation. Both the marked differences in vagus nerve diameter and fascicular organisation make it challenging to appropriately design a translatable interface for this nerve, although the NHP model can be valuable for the testing of chronic implant stability. From both an economic and ethical standpoint, NHPs should only be used when development of an interface is sufficiently advanced so that the benefits of this model, like complex movement behaviour and similarity to human biomechanics, are scientifically mandatory.

5.2. Chronic recordings

There are several points during a chronic experiment when neural recordings can be made: during implantation surgery, whilst awake and freely moving, and during termination surgery. There is intrinsic value in each of these, and many breakthroughs in the chronic stability of the neural interfaces have been made without recording from an awake animal-see for example the recording of bladder activity in rats [ 49]. However, it is often the goal to record neural signals whilst the subject is awake and freely moving. The challenges associated with doing this are multiple and span from husbandry and care to electrical interference and noise.

Fundamentally, the recording system needs to measure the potentials at the electrodes and communicate this to a remote device for processing or logging. The most straightforward way of achieving this is via percutaneous connectors; implanted cables are routed from the electrodes to the connector, and external instrumentation can then be connected on an ad-hoc basis [ 26]. Challenges include healing of the wound surrounding the connector, risk of damage due to normal or abnormal animal behaviour, and mounting the connector at a suitable anatomic site. Electrical issues can arise from the length of the cable that may be required between the electrodes and the amplifier, or from triboelectric effects from the cables themselves [ 26].

Implanting the instrumentation is an attractive alternative to percutaneous connectors, for example using a telemeter [ 119]. In this scenario, the instrumentation, potentially including a power source and a wireless communication system, is packaged in an implantable device that is located near the recording electrodes. Data storage is likely to be limited, and in most cases a receiving device must be placed on the skin over the site of the telemeter to enable data collection.

Finally, there are fundamental challenges associated with the movement of the animal. Modern amplifiers are available with very high common-mode rejection ratios (CMRRs), and so in theory, should be more than capable of rejecting common-mode signals from nearby muscles. In practice, however, differential electrode impedances and the filter networks that might be placed before the amplifiers, will degrade the CMRR. Thus, in recordings from awake and freely moving animals, it remains very challenging to ensure that recordings are not contaminated by interference from nearby muscles.

6.1. Closed-loop interfaces

Information extracted from peripheral nerve activity can be used to improve functional or health outcomes. This relationship typically takes the form of closed-loop stimulation, where the timing, pattern and/or location of stimulation are continuously adjusted based on the measured neural activity. The translational success of peripheral nerve recording techniques is therefore closely tied to closed-loop neuromodulation systems. Several studies in animal models have demonstrated the control of FES based on limb position or state estimates derived from peripheral nerve recordings, using both extraneural [ 120, 121] and intraneural [ 122] recordings. Early examples in humans included the regulation of grasping strength in a hand neuroprosthesis based on volar digital nerve feedback [ 8], and foot-drop correction using sural nerve recordings [ 123], both using nerve cuff recordings.

More recent work in humans demonstrated decoding of motor intention combined with sensory stimulation, using slanted microelectrode arrays [ 14]. While most of these efforts focused on neuroprosthetic applications, a study by Plachta et al is noteworthy for demonstrating an application related to bioelectronics medicine, namely the control of blood pressure using selective recording and stimulation of the vagus nerve [ 16].

Despite these examples, the number of studies that have demonstrated closed-loop stimulation based on peripheral trunk recordings remains low, although a substantial number of studies cite this concept as their motivation. This gap can be partially attributed to the challenges with obtaining informative and stable peripheral nerve recordings. Another notable challenge is the need for strategies for removing stimulation artefacts to effectively coordinate recording and stimulation. Artefact removal in this context may require a combination of hardware (e.g. blanking systems to prevent amplifier saturation during stimulation) and signal processing components [ 124], which increases the complexity of the instrumentation required and contributes to the low number of studies. Nonetheless, the variety of techniques described in this article and the recent acceleration of the field suggest that we are at a turning point in this regard and artefact rejection signal processing schemes that demonstrate a 25-40 dB rejection in the artefact are now available [ 125].

6.2. Autonomic nerve applications

Most of the techniques described in this manuscript have been developed for sensorimotor applications, such as the control of prosthetic limbs. However, there is a growing interest in the ability to record from smaller fibres within the autonomic nervous system (such fibres are frequently of interest in pain research). Recent advancements in hardware (e.g. more compact electrodes and multiple electrode contacts) have led to opportunities for recording from these small autonomic nerves. Thinly myelinated and un-myelinated axons (e.g. C-fibres) conduct APs more slowly (<2 m s −1) than larger fibres, and the resulting AP and CAP amplitudes are much smaller. Thus, it is challenging to record C-fibre activity without employing a very selective interface (e.g. micro-needles [ 126]) or using supra-maximal stimulation to recruit all of the C-fibres simultaneously and thus maximise the amplitude of the resulting eCAP.

Microneurography, being the easiest way to identify single C-fibre APs, employs a needle that is able to record single or compound APs [ 127]. This approach allows for unambiguous recording and recognition of C-fibre APs. Microneurography is beneficial for developing a better mechanistic understanding of peripheral nerve encoding and modulation but is not suitable in most cases for chronic use.

Novel electrode designs have also been developed for the purposes of recording from small-fibre nerves, but novel signal processing techniques for obtaining small-fibre activity are limited [ 128- 130]. This is mainly because validation of small-fibre activity is extremely challenging due to the low signal amplitude and the slow conduction velocity. Velocity selective recording (VSR) may be of significant benefit in this application as the enhancement of the SNR of the recorded signal will maximise the likelihood of observing APs from small-fibre activity, and both the conduction velocity and the direction can be validated. After locating these small-fibre APs using VSR, template matching and spatiotemporal signatures may be able to separate activities of interest, but no studies using signal processing techniques have shown this possibility.

Interfacing with these slower-conducting nerve fibres will require new or adapted recording and analysis methods. These techniques will need to either enhance the SNRs of the recorded signals to address the low signal amplitudes or be able to isolate small-fibre activity. It is likely that a combination of novel electrode design and signal processing approaches will be needed to overcome these challenges to facilitate the growing interest in recording from the autonomic nervous system.

6.3. Alternative paradigms

Electrical recording remains the dominant modality for recording from the PNS. Whilst neuromodulation via direct electrical stimulation is common (including in clinical applications), few, if any, clinical devices record from peripheral nerves. This is despite a clear need driven by the desire to achieve closed-loop, time-invariant, neural interfaces within the PNS.

There has been some (limited) engagement with active modalities (such as EIT) and with optogenetics [ 131, 132], but conventional electrical recording using either intraneural or extraneural electrodes remains the dominant modality. Many nerve interfaces (such as cuffs) are claimed to be chronically implantable-however, these claims require further verification. Stimulation electrodes need only be placed close to the target nerves, and so a cuff can be placed quite loosely around the nerve. Conversely, recording electrodes need to be placed in direct contact with the epineurium (to maximise recorded signal amplitudes), and so have the potential to cause considerable damage when implanted long-term. There are few [ 26, 48] studies describing the results of chronic implantation of recording electrodes.

At the same time, closed-loop neuromodulation is complicated by the fact that large stimulation artefacts-caused by current flowing from the stimulation electrodes onto the recording electrodes-often saturate the recording amplifiers. Some solutions to this problem exist in the form of either signal processing or electrode balancing, but the problem remains.

A novel approach is to record the magnetic fields produced by the axons rather than the electric field. This would have the benefit of not requiring any direct contact with the nerve, as the magnitude of the observed magnetic field is not related to the contact impedance, instead the location is driven by the SNR required. This concept of magnetoneurography has been demonstrated both ex-vivo and in-vitro. Okawa et al used large arrays of superconducting quantum interference devices to record neural activity transcutaneously from both the brachial plexus and the carpal tunnel area during electrical stimulation [ 133, 134]. In the case of the brachial plexus, it does not appear that any stimulation artefact was present, but in the case of the carpal tunnel area, the authors were forced to use the most distal stimulation site and to employ an artefact rejection tool (common spatial pattern). Barbieri et al performed an in-vitro demonstration of recording from mouse muscle, and also developed a model for the magnetic field contribution from the two axial components in the case of a muscle bundle [ 113]. They recorded using giant-magnetoresistance (GMR) sensors and stimulated with a suction pipette. Despite the proximity of the stimulation electrode and the GMR sensors, they reported no stimulation artefacts.

Interest in recording from the PNS continues to be growing, and as more advanced techniques are developed, it becomes more challenging to know which method to adopt. Thus, this tutorial has introduced a taxonomy of electrode configurations and recording techniques that will support the selection process and can be augmented as new methods are developed. The important aspects of selecting an appropriate animal model, as well the challenges associated with acute and chronic recordings, have been introduced. This tutorial provides a good foundation in peripheral nerve recordings for both the novice and the experienced researcher as the field continues to develop.

No new data were created or analysed in this study.

No funding was provided in support of creating this manuscript.

No new data were created or analysed in this study leading to no ethical protocol requirement.

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Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system

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, , and * Journal of Neural Engineering , Volume 19 , Number 4 Citation Ryan G L Koh DOI 10.1088/1741-2552/ac7d74 et al 2022 J. Neural Eng.

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refers to the ability of the peripheral nerve interfaces (PNI) to stimulate, or record from, specific axons, fascicles, or nerves, whilst being insensitive to off-target axons, fascicles, or nerves. Depending on the application, the desired stimulation selectivity may differ from the recording selectivity; for example, two different electrodes may be used to stimulate and record in the same prosthesis and the overall selectivity may vary from one application to another.

R. G. L. Koh, J. Zariffa, L. Jabban, S.-C. Yen, N. Donaldson, and B. W. Metcalfe, ‘Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system’, Journal of Neural Engineering, 2022.

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, , , , and

*
Journal of Neural Engineering , Volume 19 , Number 4 Citation Ryan G L Koh DOI 10.1088/1741-2552/ac7d74 et al 2022 J. Neural Eng. 19 042001

Author affiliations

¹ KITE, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, M5G 2A2, Canada

² Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada

³ Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M4G 3V9, Canada

⁴ Rehabilitation Sciences Institute, University of Toronto, Toronto, ON M5S 2E4, Canada

⁵ Centre for Biosensors, Bioelectronics, and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom

⁶ Innovation and Design Programme, Faculty of Engineering, National University of Singapore, Singapore, Singapore

⁷ The N.1 Institute for Health, National University of Singapore, Singapore, Singapore

⁸ Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6DH, United Kingdom

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⁹ Author to whom any correspondence should be addressed.

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Abstract

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of PNIs, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

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Neural control is at the heart of our agency in the world. Through a combination of conscious and automatic processes, the nervous system enables us to regulate ourselves and our interactions with our environment. As we seek to modulate, restore, or enhance these abilities, we have turned in recent decades to technologies that can interface effectively with the nervous system. Certain applications benefit from interfaces at the level of the central nervous system (CNS), for example, when dealing with brain disorders such as Parkinson's, psychiatric conditions [ 1, 2] or attempting to coordinate patterns of movement through spinal circuitry [ 3, 4]. In many cases, however, the peripheral nervous system (PNS) offers the advantage of carrying afferent or efferent information that most directly relates to the function of the target of interest.

For most of its history, this field has focused on restoring function lost as a result of amputation [ 5- 7] or paralysis [ 8, 9]. More recently, the scope has broadened considerably (as reviewed in [ 10]), including significant interest in chronic disease applications involving the autonomic nervous system [ 11- 13]. These developments have gone hand-in-hand with new progress in neural interfacing technologies and have spawned the related field of bioelectronic medicine.

Peripheral nerve interfaces (PNIs) can modulate neural activity through stimulation and/or monitor neural control and feedback through recording. Key potential applications of peripheral nerve recordings include the control of prosthetic limbs through extracted motor commands [ 14], the closed-loop control of functional electrical stimulation (FES) systems through extracted afferent information [ 15], and the neuromodulation of body functions through the identification of autonomic control signal [ 16] and electrical disease biomarkers [ 17].

Achieving stable and functional recording in the PNS has been a persistent challenge in neural engineering for several reasons-many of which are distinct from those faced by CNS interfaces. First, in peripheral nerves, the nerve fibres (axons) are tightly packed into fascicles, which are in turn held together by connective tissue to form the nerve trunk [ 18]. The whole structure, in many cases, has a diameter on the order of hundreds of microns to a few millimetres. In this configuration, the axons are densely organised into a relatively small-diameter structure, making it difficult to isolate activity related to a particular function. Second, sparse firing patterns and the lack of large, synchronised populations result in small-amplitude signals. Third, many peripheral nerves experience significant movement during physical activities (e.g. during respiration in larger mammals), and are often located close to muscles whose bioelectrical activity creates substantial interference. Lastly, in the case of chronic implantation, encapsulation tissue can form in and around an electrode and alter the nerve-electrode interface and thus the amplitude and nature of the recorded signals [ 19].

Several device designs have been proposed to create reliable PNIs and these are divided into intraneural and extraneural. The former involves penetration of the device into the nerve trunk, while the latter relies on devices positioned on or near the surface of the trunk [ 20, 21]. These approaches have been considered in a trade-off between selectivity and invasiveness.

Regardless of the approach, multi-channel PNI designs are increasingly providing new possibilities to extract detailed information about the neural function. The availability of multiple channels provides signal processing opportunities for resolving ambiguities that cannot be dealt with in a single channel, and enables powerful machine learning or regression approaches [ 14, 22- 29]. Several recent reviews have covered peripheral nerve electrode designs [ 20, 21, 30, 31], but reviews of the data analysis techniques necessary for the creation of effective PNIs are limited [ 32].

The objective of this tutorial article is to provide a resource that captures the key methods for the analysis of peripheral nerve recordings, and specifically examines the critical interplay between the type of electrode used, the signal analysis techniques applied, and the nature of the information extracted. Further guidance is also given on the importance of appropriate experimental models and the challenges associated with chronic implantation.

The structure of this paper is as follows. Section 2 will introduce general principles and typical approaches to making recordings. Section 3 will introduce the most common electrode geometries and discuss their impact on the nature of information extracted, section 4 will describe the most common signal processing approaches, section 5 will discuss experimental and translational considerations, and section 6 will identify future trends and technologies.

It is important to understand the general principles of a PNI recording system before considering the relative merits afforded by different configurations. The following sections will briefly introduce the properties of peripheral nerve recordings as well as general principles in acquiring and pre-processing these signals.

Figure 1 shows a simplified overview of a typical PNI. Electrodes are placed near, on, or in, the nerve trunk and are connected to low-noise amplifiers (LNAs) and a digital signal processing (DSP) system. The neural signals occurring within the nerve are thus amplified, digitised, and processed to provide an output that can take several forms including: spike trains, continuous time series, and images showing the source of the neural activity within the nerve trunk. This information can then be used to inform a prosthesis such as an artificial limb or a neuromodulation device. It is important first to have an understanding of the nature of neural signals, and so a brief overview will now be given.

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2.1. Biophysics of neural signals

Individual axons produce action currents with magnitudes in the pico-ampere range, due to APs, which may be detected as neural signals. These small action currents give rise to small potentials (on the order of 1 µ V), that are difficult to detect in the presence of noise and interference. There are two ways to obtain a detectable potential: an electrode must be tiny and in close proximity to the axon, so that the potential is produced across the spreading resistance from a node of Ranvier, or there must be a restricted extra-cellular space that creates a high resistance through which the small action currents flow. These two cases give rise to a taxonomy wherein an interface may be defined as operating with either unrestricted (e.g. intraneural) or restricted (e.g. extraneural) extracellular space.

Neural signals that occur spontaneously (i.e. without external stimulation or modulation) are composed of individual APs resulting from normal biological functions. Typically, neurons within different fascicles innervate unrelated tissue, and do not fire synchronously. Consequently, the observed neural signal is characterised by low amplitude and high frequency activity.

Spontaneous signals recorded extraneurally (i.e. within a constrained extracellular space) from the surface of the nerve trunk are rarely larger than 30 µV peak-to-peak [ 33], whereas intraneural signals recorded from inside the trunk can be over 100 µV peak-to-peak [ 34]. Most of the signal power is concentrated in the range of 300 Hz-5 kHz, with the peak below 3 kHz [ 35- 37].

The small amplitude of the neural signal creates significant challenges, especially against the background of instrumentation noise (on the order of 2-4 µ V root-mean-square (RMS)). Adding considerably to this challenge is the interference from nearby muscles (electromyographic (EMG) activity), which can be an order of magnitude larger than the neural signal when both are recorded using a monopolar reference. The EMG bandwidth (approximately 5-500 Hz) has partial overlap with the neural signal, precluding effective removal using linear filtering without loss of information.

Neural signals may also be directly evoked, or modulated, by mechanical, chemical, or electrical stimulation. When this occurs, many axons produce APs simultaneously, and the resulting neural signal (the evoked compound AP (eCAP)) is the result of the superposition of these APs. In this case the amplitude of the eCAP will be much larger than that of a single AP (∼100 µV for cuffs), see figure 6(a) for an exemplar recording of both spontaneous APs and eCAPs.

2.2. Amplification and acquisition

At the amplification and acquisition stage, a well-chosen reference montage can be used to minimise the contributions of interfering sources. The most widespread example of this approach is the tripolar arrangement, in which the signals at the end electrodes are averaged and used as a [ 33]. This configuration helps suppress EMG interference by taking advantage of the linearisation of these electric fields along the length of the recording array [ 38], and can be implemented through several alternative differential recording arrangements [ 39]. For intraneural recordings both monopolar and bipolar approaches have been reported [ 40, 41]. Examples of the monopolar, bipolar, and tripolar amplifier configurations are given in figure 2.

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Acquisition is generally less critical, and can be performed by most good quality analogue-to-digital converters with sufficient sampling rates (typically >30 kHz), although techniques that measure the conduction velocity of the neural signals may require supra-nyquist sample rates to adequately sample fast APs if the inter-electrode distance is small.

2.3. Signal pre-processing and denoising

Noise and interference in neural recordings can arise from a several sources such as interfering muscle activity [ 38], movement artefacts causing audiophonic noise or triboelectric noise, noise from a high impedance ground, or electromagnetic noise [ 26]. Using the appropriate signal processing techniques can help minimise noise and interference.

At the data pre-processing stage, bandpass filtering is commonly applied to isolate the neural signal, with a high-pass frequency in the 250 Hz-1 kHz range and a low-pass frequency in the 3-7.5 kHz range. If the objective is simply to detect the presence of neural activity, a rectified-bin-integration (RBI) approach can be applied, in which windows that contain neural activity, as well as noise, produce higher values than those that only contain noise [ 42]. This approach, however, is accompanied by a loss of temporal resolution that may preclude the use of many of the techniques described in the following sections.

An additional and more sophisticated pre-processing option is wavelet denoising, which relies on transforming the noisy data into an orthogonal time-frequency domain, thresholding the wavelet coefficients to remove the noise, then transforming back to the original time domain. Selection of the mother wavelet and thresholds are key considerations, but reports have varied about which choices are optimal for neural signals [ 35, 41, 43].

The geometry and configuration of the electrodes used to obtain a recording are critical in determining the type of signals that can be measured. Selecting the most appropriate electrode material, structure, configuration, and geometry for a specific application requires an in-depth understanding of the advantages and disadvantages of each. Ciancio et al [ 44] have summarised these requirements, and from their work and the wider literature it is possible to identify two key characteristics of a PNI.

Selectivity-Selectivity refers to the ability of the PNI to stimulate, or record from, specific axons, fascicles, or nerves, whilst being insensitive to off-target axons, fascicles, or nerves. Depending on the application, the desired stimulation selectivity may differ from the recording selectivity; for example, two different electrodes may be used to stimulate and record in the same prosthesis and the overall selectivity may vary from one application [ 7] to another [ 45].

Stability-Equally important is the stability of the PNI, both for acute and chronic applications. It should be stable over time and should inflict as little physiological or histological damage to the tissue as possible [ 46]. These properties are governed by factors such as the mismatch of mechanical properties between the tissue and the electrode, as well as the immunological reaction of the tissue to different materials and surface treatments [ 47].

3.1. Electrode location

3.1.1. Extraneural electrodes

One exception to this is the microchannel interface, in which the nerve trunk may be micro-dissected into fascicles that may then be placed within channels that constrain the extracellular space [ 49]. This results in fewer axons per channel and typically higher signal amplitudes (∼100 µ V).

3.1.2. Intraneural electrodes

Intraneural electrodes are normally implanted directly inside the fascicles, penetrating the perineurium, and show better selectivity than extraneural electrodes as they have closer contact with the fascicles. Examples of these include thin-film longitudinal intrafascicular electrodes (LIFEs) [ 50], and transverse intrafascicular multi-channel electrodes (TIMEs) [ 51]. In terms of stability, their invasiveness is higher than extraneural electrodes and the implantation itself may cause damage [ 21, 52, 53].

3.1.3. Regenerative interfaces

Regenerative interfaces are often designed to interface with small groups of axons, allowing for selective stimulation and recording with the best possible level of selectivity. Instead of penetrating the nerve with the electrode, the nerve is transected and then supported to regrow through a structure containing electrode channels [ 54]. In one embodiment, the structure resembles a sieve consisting of a piece of material with multiple micropores covered with a conductive material. After transection, each end of the nerve is placed on either side of the sieve and the axons grow through the micropores. A schematic representation of this is shown in figure 2.

It has been shown that neurons will regenerate through the sieve structure, and the sieves are functional as both recording and stimulation devices with high selectivity. However, regenerative electrodes may result in incomplete or constrained regeneration of axons, leading to difficulties with chronic implantation [ 55, 56].

3.1.4. Electrode configuration

Several recent papers review the biocompatibility and stability of different electrode materials and structures [ 31, 57]. However, from the point of view of neural recordings, it is helpful to consider a taxonomy of geometries-as it is the spatial relationship between the signal source and the electrodes, alongside the constraining of the extracellular space, that often defines the recording selectivity and capability.

Electrodes may be grouped to form point measurements (single channel), one-dimensional linear arrays (organised circumferentially, transversely, or longitudinally), or two-dimensional linear arrays. Figure 3 illustrates these geometries using an extraneural cuff interface. The substrate (i.e. insulating tube) of the cuff and FINE interfaces constrains the extracellular space and serves to maintain the spatial relationship between the electrodes. Intraneural electrodes may, or may not, have stable fixation and so while the geometries are applicable to all interfaces, the spatial stability should also be considered.

3.2. Point measurements

One-dimensional point measurements are by far the most common and lend themselves to a wide array of signal processing techniques. Point measurements, in this paper, refer to measurements produced from a single recording channel (i.e. an observation at a single spatial location) that may have been referenced by a monopolar, bipolar, or tripolar configuration.

3.3. One-dimensional arrays

One-dimensional arrays can be formed by placing multiple electrodes either longitudinally, transversely, or circumferentially. Thus, both temporal and spatial classification becomes possible.

Temporal classification-relies on the fact that the propagation of the AP may be observed by placing an array of electrodes located longitudinally along the length of the nerve. Different properties of the APs, such as conduction velocity (which is proportional to axon diameter), may then be used to discriminate APs from individual axons, or types of axons [ 58, 59]. These approaches do not require any prior knowledge about the AP morphology, and many can improve the signal-to-noise ratio (SNR) of the recording by averaging over multiple recording channels.

Spatial classification-relies on the fact that an array of electrodes arranged circumferentially can selectively identify activity from within different fascicles or axons, based on the spatial location of each electrode with respect to the neural source. Passive recording approaches include source localisation and types of beamforming, many of which do not require prior knowledge about the expected morphology of the AP but do require high SNRs to localise activity to individual fascicles [ 60].

3.4. Two-dimensional arrays

Circumferentially and longitudinally spaced electrodes can be combined to form a two-dimensional structure that enables the observation of APs in both space and time [ 61].

Spatiotemporal classification-combines the benefits of longitudinal and circumferential arrays for a more comprehensive and robust characterisation of the APs. Classification can be performed using templates, or by training a convolutional neural network (CNN) to recognise the spatiotemporal patterns associated with specific neural activity [ 22].

Section 3 introduced a taxonomy of electrode geometries and explained how the choice of geometry impacts the available signal processing methods-including temporal, spatial and spatiotemporal. Several key analysis techniques will now be introduced and discussed in the context of content extraction. The information of interest in peripheral nerve recordings can be broken down more broadly into two categories: anatomical and functional.

4.1. Anatomical content

Anatomical content pertains to the size, shape, type, and positions of structures within the nerve. In terms of PNI, anatomical information can be provided by making observations of the propagation direction (afferent versus efferent), axon type (e.g. A

Throughout this paper, direction sensitivity refers to the ability to discriminate afferent versus efferent, velocity refers to the conduction velocity of each AP, and location of neural sources refers to the determination of the spatial location of the source of the neural signal within the nerve. Anatomical content is most readily obtained using extraneural electrodes, as they provide a macro view of the activity within the entire nerve, as opposed to intraneural electrodes that offer a more microscopic (and thus spatially localised) view.

4.2. Functional content

Functional content within a nerve pertains to a specific organ or system. For example, signals within the ulnar nerve may encode sensation (via cutaneous afferents) from the hand and forelimb. The overall presence (e.g. a non-selective power measurement) of neural activity can also be considered functional content. Functional interpretation of a signal can be made without knowledge of the anatomical underpinnings (e.g. where exactly in the nerve an axon is located) but does require knowledge of the nerve's innervation.

A common approach to extracting functional content is to identify single unit activity (i.e. APs that result from specific axons) in a process called spike sorting, wherein individual APs are grouped into clusters based on morphology. The resulting single unit labels (spike trains) can then be used directly in a neuroprosthesis without reference to any anatomical organisation. Spike sorting is most used in intraneural electrode configurations wherein the SNR is high and individual APs can be observed. Figure 4 illustrates this process for exemplar data with three active axons.

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More generally, multi-unit single axon AP trains, eCAPs, or signal windows from either intraneural or extraneural recordings can be associated with a particular function of interest through classification or clustering approaches. In this paper the task of associating neural signals with different functional events is termed discrimination of neural pathways.

In some cases, certain information can overlap anatomical and functional content. For example, the location of neural sources refers to the spatial location of the source within the nerve, but with some a priori information, a functional aspect can be determined. In the popular rat sciatic nerve model, identifying neural sources in the tibial nerve would correspond to dorsiflexion of the ankle while identifying neural activity in the peroneal nerve would correspond to plantarflexion of the ankle.

Deciding what information (anatomical vs functional) to obtain depends largely on the application of interest. If the intended use is focused on understanding the underlying physiology, anatomical content will likely be more applicable. On the other hand, functional content may be of more interest if the intended use is to obtain a control signal (i.e. to use in a neuroprosthetic device).

4.3. Content extraction techniques

Once the information of interest is known, the appropriate electrode configuration and data analysis techniques can be employed. Figure 5 introduces a taxonomy of the different methods and the applicable electrode configurations that can be used to determine anatomical or functional content. For example, if one is interested in imaging (producing a cross sectional image with the location of neural sources identified), then a 1D-circumferential cuff could be employed alongside the electrical impedance tomography (EIT).

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4.3.1. Time series analysis

The most common way of analysing neural signals still lies in using time-domain, frequency-domain, or statistical features without necessarily relying on any aspect of the anatomical information (i.e. fibre velocity, spatial location) directly.

Time domain techniques to smooth the neural signal over a window can be used to detect neural activity [ 62]. The most common approach is the RBI operation, where the signal is rectified, binned into time windows, and integrated. This technique extracts the signal's envelope and enables a simpler and smoother signal for analysis compared to the noisier raw recordings. This signal can then be thresholded to determine if a neural source of interest is active.

Another approach for identifying neural activity is to observe the statistical properties of the signal and noise. For example, the autocorrelation matrix of white noise will be diagonal because the samples are completely uncorrelated. Therefore, the eigen-decomposition of this matrix will yield a single non-zero eigenvalue. In contrast, a recording containing both neural data and noise will have off-diagonal elements in the autocorrelation matrix, and thus more than one eigenvalue. The difference between the greatest and smallest eigenvalue can be used to detect neural signals [ 42].

These techniques are simple and effective for detecting neural activity but alone are often inadequate for obtaining more sophisticated functional or anatomical content. However, if the dominant application is to detect neural activity, these techniques are typically sufficient and can be quickly and easily implemented.

4.3.2. Choice of reference

Reference choice has a significant impact on the recording quality of a PNI as it can attenuate interfering signals and can also be used to determine the direction of propagation (i.e. afferent vs efferent signals) without the need for other techniques or methods.

4.3.3. Template matching

The main advantage of template matching is that it is amenable to implementation on an online system, but its effectiveness is reduced if the templates are similar or the number of distinct sources increases. Another drawback occurs when AP shapes overlap in time [ 68] and thus choosing templates that represent the underlying neural activity may not be straightforward. The use of multi-contact electrodes has helped mitigate the effects of this issue.

4.3.4. Velocity selective recording

The concept of velocity discrimination is founded on the fact that the conduction velocity of an AP is a function of the axon properties, all of which are assumed to be either time invariant (e.g. myelin thickness, diameter, membrane properties) or tightly regulated (e.g. temperature, ionic concentrations) [ 69]. The axon diameter and myelin's presence (or lack thereof) are the main factors that determine the difference in conduction velocity from one axon to another.

The velocity of an AP can be computed by delaying the signals recorded from each element of a longitudinal array relative to one another by an interval that corresponds to the conduction velocity before summing together [ 70, 71]. One advantage of this process is the ability to distinguish afferent and efferent neural activity by simply applying a negative delay.

The first demonstrations of velocity selective recording were made using electrical stimulation to recruit large amplitude eCAPs, in worm [ 71] frog, pig, and rat [ 27, 28, 72- 74]. The delay-and-add process is used to provide a spectral representation of eCAP recordings, where the spectrum is presented with respect to velocity rather than frequency. Figure 6(a) shows a time domain eCAP recording and the corresponding velocity spectrum.

4.3.5. Source localisation and beamforming

Most, if not all, techniques for locating neural sources derive from source localisation and beamforming techniques that are related to inverse problems in electroencephalography [ 75]. Briefly, the inverse problem of bioelectric source localisation is based on the equation:

where d is an M ×1 vector containing the recorded data from M electrode contacts, j is an N × 1 vector whose entries represent the magnitude of the current dipoles distributed in the region under consideration, and L, known as the lead field matrix, is an M × N matrix whose entries represents the influence of a unit current dipole on the potential recorded at a particular electrode. is an M × 1 vector of additive noise.

The objective is to recover j based on the measurements of d and estimate of L.

Beamforming is a signal processing technique that uses spatial filters that combine recordings made at different spatial locations to enhance (via constructive interference) the selectivity of the recordings. When tuned correctly, this can be used to localise neural sources within the nerve. The advantage of this technique is that it can provide both anatomical (the location of the neural activity within the nerve) and, with some a priori information, functional content. However, solving the inverse problem is non-trivial and in small nerves the neural sources of interest are much closer in distance, reducing the ability to localise them.

Most source localisation approaches have been attempted with extraneural electrodes [ 25, 26, 29, 76- 82]. An approach using FINE electrodes has shown the most promising results, demonstrating the feasibility of real-time implementation in chronic recordings involving canines [ 26, 82].

4.3.6. Spatiotemporal signatures

The main advantage of this technique comes from the direct integration of the temporal and spatial information allowing for a more comprehensive characterisation of neural activity. These spatiotemporal signatures have been used as an input to a CNN which demonstrated the ability to discriminate naturally evoked CAPs (nCAPs) and be used to reconstruct firing patterns of different neural pathways [ 22]. This network is known as the extraneural spatiotemporal compound AP extraction network or ESCAPE-NET. The overall ESCAPE framework can be seen in figure 7 alongside an example of a spatiotemporal signature.

This technique has shown promising results in an acute rat model. A recent simulation study [ 84] aimed at mimicking chronic conditions suggested that the selectivity of the spatiotemporal signature can be maintained by establishing a recalibration schedule for the classifier.

4.3.7. Electrical impedance tomography (EIT)

EIT is a method that can provide fascicular-level selectivity with an extraneural approach using electrodes distributed around the nerve. EIT is an emerging medical imaging technique in which changes in the impedance of a conductive volume, such as a nerve, may be imaged using an array of external electrodes [ 85, 86]. In this method, a flexible, cylindrical, multi-electrode cuff is placed around a nerve, and the imaging technique of fast EIT is applied to image the activity within the fascicles. Changes in impedance caused by small decreases in bulk tissue resistance occur as ion channels open and close, and these can be detected (with some averaging) using the external electrodes.

Mathematically, EIT is similar in principle to inverse source localisation. However, EIT has significant advantages including: more independent data (for N electrodes, there are O(N ²) independent measurements at a time compared to O(N) for inverse source localisation), a potentially unique solution, no field cancellation problem, and no theoretical limitations on the accuracy [ 87- 89].

The principle of operation is that small currents (typically 30 µA at 6 kHz) are injected between different pairs of electrodes, while the resulting voltage is measured at every other pair. This process is repeated over all electrodes to produce a set of measurements that can be re-constructed into an image of the nerve. Figure 8 illustrates the principle of fast neural EIT in a model peripheral nerve. An impedance change associated with neural activity occurs in figure 8(b) relative to the baseline cases in figures 8(a) and (c). This change in impedance can be observed as a change in the measured voltage of a pair of electrodes to a current applied in another pair of electrodes in figure 8(d). Multiple measurements of this change can be used to produce an image of the activity in figures 8(e) and (h). EIT is an effective recording approach for eCAPs, wherein the averaging process can readily be performed, but has yet to be demonstrated with spontaneous neural signals.

The signal processing and recording methods presented in this paper are given as examples, and new methods will undoubtedly be developed in due course. However, the taxonomy in figure 5 should provide the reader with a powerful tool for selecting the electrode geometry and thus the type of information that can be extracted by the different methods. Having now covered the fundamentals of neural recording and introduced a range of electrode geometries and signal processing methods, the following section will cover the translational considerations and experimental challenges associated with animal models.

The previous sections have introduced the fundamentals of peripheral nerve recordings, from the biophysics of the APs, through the electrode geometry, and then the signal processing. This section will introduce the animal models typically used to develop and test PNIs and discuss the potential translational issues using the context of the cervical vagus nerve as an example target.

5.1. Experimental models

Most of the development of neural interfaces is performed using animal models, in either acute or chronic experiments. There are both ethical and scientific reasons for using animal models; however, these models also lead to several associated translational challenges. A recent detailed review of animal models used for PNI development can be found in Aman et al [ 91].

5.1.1. Acute experiments

In acute surgical experiments, the electrodes (recording, stimulation, or both) are implanted in anesthetised animals and the target nerve is explored over the course of a few hours whilst the animal remains unconscious [ 17, 25, 72, 83, 92, 93]. The animal is then terminated at the end of the experiment. Acute experiments are beneficial because they provide a stable platform for interrogating the nervous system that includes any and all organs or tissues of interest, whilst removing several of the challenges associated with chronic experiments such as movement artefacts and the need for either percutaneous connectors or a wireless telemetry system [ 38].

However, the anaesthetic regime employed for the surgery has an influence on the electrophysiology that remains unclear, even though the molecular mechanisms of anaesthetics are well characterised. For example, it is known that both isoflurane and ketamine, two widely used anaesthetic paradigms, differentially impact sensory processing in the mouse primary visual cortex [ 94]. The complex interactions between the anaesthetic agents, and their compound effect on the electrophysiology of the animal, remain problematic and may hinder the direct translation of results to awake animals.

5.1.2. Chronic experiments

In chronic experiments the electrodes are implanted under general anaesthesia, but the animals are then recovered for a period of time (from days to months) [ 26, 95- 97]. Recording may take place during the implantation surgery, while the animal is awake and freely moving, or during a termination surgery. The former and the latter have the advantage of not requiring percutaneous connectors or a wireless telemetry system but suffer the same drawbacks as acute experiments. Recordings made whilst the animals are awake and freely moving are, for many applications, the most representative but also by far the most challenging. One advantage of using percutaneous connectors is that measurements of the electrode impedances may be made directly, and then related to the recorded signal amplitudes.

It is important that an appropriate species be chosen for the model, based not just on the ease of the experiment but also on any translational opportunities or issues. If the goal of the experiment is to elucidate some fundamental electrophysiologic property or phenomena, then a well-characterised species such as mice or Xenopus Laevis frogs may be suitable. Likewise, the development of electrode materials and the associated instrumentation may not depend greatly on the species and so mice or rats may be chosen based on reasons of cost or simplicity. However, if the goal-at any point in time-is to translate the research into humans, then the choice of model is critical. The difference between the peripheral nerves of humans and other mammals is not obvious; differences may exist in gross anatomy and geometry of the nerves, the type and level of fascicular structure and vascularisation, and the distribution of myelinated versus unmyelinated axons [ 98, 99].

A good example of the difference between species can be found by considering the cervical vagus nerve. The vagus is the tenth cranial nerve and for many years has been the focus of significant interest as a neuromodulation target for the treatment of diseases as broad-ranging as intractable epilepsy [ 100, 101], diabetes [ 102, 103], and rheumatoid arthritis [ 104- 106]. Figure 9 shows the comparative anatomy of the cervical vagus nerve between mouse, rat, canine, non-human primate (NHP), pig and human [ 90]. Each of these will be discussed in the context of the individual animal model.

5.1.3. Rodents and small animals

Rodents, typically mice or rats, have been the staple model for biomedical research for a long time. They are well characterised, easy to care for, and have the benefit of being available in a multitude of different inbred or outbred strains. From an experimental perspective, they can be anaesthetised and maintained without complex anaesthetic equipment. Indeed in rats and mice it is commonplace to use only a single intraperitoneal administration of an agent such as urethane [ 107]. The peripheral nerves are, of course, physically much smaller than in humans. On the one hand this can make the surgical approach to the nerves relatively straightforward, as there is minimal tissue or bone to remove. On the other hand, it makes the design and placement of electrodes far more difficult, thus limiting the number of electrodes that can be placed as well as the separation between stimulation and recording electrodes. The latter point is important as an increased separation between the stimulation and recordings sites is one method for reducing or eliminating stimulation artefacts from recordings.

Animals such as guinea pigs, cats, and rabbits are an attractive middle ground between rodents and larger animals but are much less explored in the literature on electrophysiology. Cats have seen the greatest attention [ 65, 108, 109]. Aside from potential inexperience in working with these species, there is likely to be a stronger perceived ethical concern associated with species that are companion animals. Interestingly, this has led to the implantation of neuromodulation devices in companion animals as a therapy, for example the Brindley device has been implanted in companion dogs to restore urinary bladder control [ 110].

Turning to the cervical vagus as an example target, it is smaller in mice () than in rats (), and typically consists of only 1-2 fascicles (figure 9(a)). The canine vagus is larger again () and has the thickest epineurium across most models, resulting in a greater distance from either the stimulation or recording electrodes placed on the epineural surface to the axons. This in turn would likely alter the stimulation thresholds and the amplitudes of the recorded neural signals. In the case of mice, rats, and canines, the fascicular structure is far less complex than that in humans-further complicating any translation of design parameters from one to the other.

5.1.4. Large animals

Large animals-in this context sheep and pigs-have long been common-place in neuromodulation research, although their prevalence is in part limited by their comparatively complex requirements for both housing and surgical management. Unlike rodents, sheep and pigs are generally not as well characterised, and are typically obtained from commercial farmers or kept in small groups for breeding purposes. Their housing and husbandry requirements are not necessarily more difficult than those of rodents but do require greater space and nutritional resources. Pigs in particular are frequently reported in experimental research and have been for many decades [ 72, 73, 101, 111].

Compared to rodents, the surgical approaches to the peripheral nerves of pigs and sheep can be more complex and time consuming, although the gross anatomy and the approach, in general, have the potential to be more directly translated to humans. Anaesthetic induction and maintenance is complex, and almost always requires a dedicated anaesthetist and mechanical ventilation providing volatile anaesthetics such as sevoflurane or isoflurane [ 72]. Agents such as fentanyl, propofol, and ketamine may be administered before or during surgery, typically as a bolus, although continuous rate infusion of propofol is possible [ 112]. The impact of these agents on the electrophysiology of the animals is more challenging in part due to the number of agents administered.

Returning to the vagus nerve as an example target, it is of similar size () in pigs, sheep, and humans [ 113]. The number of fascicles in the vagus nerve of pigs and sheep is on the order of 30, whereas in humans it is closer to 10. There is a similar thickness of epineurium and level of vascularisation across the three species. Thus, pigs and sheep represent appropriate models for developing electrodes, instrumentation, and surgical approaches that might be translated into humans. The distance between the axons and electrodes placed on the epineurium is likely to be similar, so stimulation thresholds and recorded signal amplitudes ought to be comparable.

5.1.5. Humans and non-human primates (NHPs)

Experiments involving either humans or NHPs are the least common, due in large part to the ethical and regulatory issues surrounding them. In humans, most experiments are performed as part of a larger rehabilitation package wherein the patient is implanted with a therapeutic device that may also collect data, or as part of a clinical trial for an innovative technology. Good examples of this include PNIs that are used for either controlling upper-limb prosthesis or for proving sensory feedback from a prosthesis-such experimental interfaces have been implanted in humans for several weeks or months [ 6, 7, 114, 115].

NHPs have predominantly been used in experiments that target the CNS, wherein single unit recordings can be made from awake, behaving animals using high-density electrode arrays [ 116]. The surgical approaches used in NHPs are most like those used in humans and other large mammals, an attribute that both aids potential clinical translation but also raises the costs and complexities of experimentation. One significant benefit with most NHPs is that they can be trained to perform purposive motor tasks (such as reaching and grasping), and this has made them popular in various areas of sensorimotor research, for example in the development of PNIs for the control of prosthetic limbs [ 117] or the use of optogenetic modulation to improve motor functionality [ 118]. However, there is often significant training time (and thus cost) associated with the development of purposive experimental paradigms [ 91].

Returning to the vagus nerve as an example target, the vagus nerve in NHPs is smaller than that of the human () with notably less complex fascicular organisation [ 113]. The number of fascicles in the vagus nerve of NHPs is typically only 1-2, but there is a similar (relative) thickness of epineurium and level of vascularisation. Both the marked differences in vagus nerve diameter and fascicular organisation make it challenging to appropriately design a translatable interface for this nerve, although the NHP model can be valuable for the testing of chronic implant stability. From both an economic and ethical standpoint, NHPs should only be used when development of an interface is sufficiently advanced so that the benefits of this model, like complex movement behaviour and similarity to human biomechanics, are scientifically mandatory.

5.2. Chronic recordings

There are several points during a chronic experiment when neural recordings can be made: during implantation surgery, whilst awake and freely moving, and during termination surgery. There is intrinsic value in each of these, and many breakthroughs in the chronic stability of the neural interfaces have been made without recording from an awake animal-see for example the recording of bladder activity in rats [ 49]. However, it is often the goal to record neural signals whilst the subject is awake and freely moving. The challenges associated with doing this are multiple and span from husbandry and care to electrical interference and noise.

Fundamentally, the recording system needs to measure the potentials at the electrodes and communicate this to a remote device for processing or logging. The most straightforward way of achieving this is via percutaneous connectors; implanted cables are routed from the electrodes to the connector, and external instrumentation can then be connected on an ad-hoc basis [ 26]. Challenges include healing of the wound surrounding the connector, risk of damage due to normal or abnormal animal behaviour, and mounting the connector at a suitable anatomic site. Electrical issues can arise from the length of the cable that may be required between the electrodes and the amplifier, or from triboelectric effects from the cables themselves [ 26].

Implanting the instrumentation is an attractive alternative to percutaneous connectors, for example using a telemeter [ 119]. In this scenario, the instrumentation, potentially including a power source and a wireless communication system, is packaged in an implantable device that is located near the recording electrodes. Data storage is likely to be limited, and in most cases a receiving device must be placed on the skin over the site of the telemeter to enable data collection.

Finally, there are fundamental challenges associated with the movement of the animal. Modern amplifiers are available with very high common-mode rejection ratios (CMRRs), and so in theory, should be more than capable of rejecting common-mode signals from nearby muscles. In practice, however, differential electrode impedances and the filter networks that might be placed before the amplifiers, will degrade the CMRR. Thus, in recordings from awake and freely moving animals, it remains very challenging to ensure that recordings are not contaminated by interference from nearby muscles.

6.1. Closed-loop interfaces

Information extracted from peripheral nerve activity can be used to improve functional or health outcomes. This relationship typically takes the form of closed-loop stimulation, where the timing, pattern and/or location of stimulation are continuously adjusted based on the measured neural activity. The translational success of peripheral nerve recording techniques is therefore closely tied to closed-loop neuromodulation systems. Several studies in animal models have demonstrated the control of FES based on limb position or state estimates derived from peripheral nerve recordings, using both extraneural [ 120, 121] and intraneural [ 122] recordings. Early examples in humans included the regulation of grasping strength in a hand neuroprosthesis based on volar digital nerve feedback [ 8], and foot-drop correction using sural nerve recordings [ 123], both using nerve cuff recordings.

More recent work in humans demonstrated decoding of motor intention combined with sensory stimulation, using slanted microelectrode arrays [ 14]. While most of these efforts focused on neuroprosthetic applications, a study by Plachta et al is noteworthy for demonstrating an application related to bioelectronics medicine, namely the control of blood pressure using selective recording and stimulation of the vagus nerve [ 16].

Despite these examples, the number of studies that have demonstrated closed-loop stimulation based on peripheral trunk recordings remains low, although a substantial number of studies cite this concept as their motivation. This gap can be partially attributed to the challenges with obtaining informative and stable peripheral nerve recordings. Another notable challenge is the need for strategies for removing stimulation artefacts to effectively coordinate recording and stimulation. Artefact removal in this context may require a combination of hardware (e.g. blanking systems to prevent amplifier saturation during stimulation) and signal processing components [ 124], which increases the complexity of the instrumentation required and contributes to the low number of studies. Nonetheless, the variety of techniques described in this article and the recent acceleration of the field suggest that we are at a turning point in this regard and artefact rejection signal processing schemes that demonstrate a 25-40 dB rejection in the artefact are now available [ 125].

6.2. Autonomic nerve applications

Most of the techniques described in this manuscript have been developed for sensorimotor applications, such as the control of prosthetic limbs. However, there is a growing interest in the ability to record from smaller fibres within the autonomic nervous system (such fibres are frequently of interest in pain research). Recent advancements in hardware (e.g. more compact electrodes and multiple electrode contacts) have led to opportunities for recording from these small autonomic nerves. Thinly myelinated and un-myelinated axons (e.g. C-fibres) conduct APs more slowly (<2 m s −1) than larger fibres, and the resulting AP and CAP amplitudes are much smaller. Thus, it is challenging to record C-fibre activity without employing a very selective interface (e.g. micro-needles [ 126]) or using supra-maximal stimulation to recruit all of the C-fibres simultaneously and thus maximise the amplitude of the resulting eCAP.

Microneurography, being the easiest way to identify single C-fibre APs, employs a needle that is able to record single or compound APs [ 127]. This approach allows for unambiguous recording and recognition of C-fibre APs. Microneurography is beneficial for developing a better mechanistic understanding of peripheral nerve encoding and modulation but is not suitable in most cases for chronic use.

Novel electrode designs have also been developed for the purposes of recording from small-fibre nerves, but novel signal processing techniques for obtaining small-fibre activity are limited [ 128- 130]. This is mainly because validation of small-fibre activity is extremely challenging due to the low signal amplitude and the slow conduction velocity. Velocity selective recording (VSR) may be of significant benefit in this application as the enhancement of the SNR of the recorded signal will maximise the likelihood of observing APs from small-fibre activity, and both the conduction velocity and the direction can be validated. After locating these small-fibre APs using VSR, template matching and spatiotemporal signatures may be able to separate activities of interest, but no studies using signal processing techniques have shown this possibility.

Interfacing with these slower-conducting nerve fibres will require new or adapted recording and analysis methods. These techniques will need to either enhance the SNRs of the recorded signals to address the low signal amplitudes or be able to isolate small-fibre activity. It is likely that a combination of novel electrode design and signal processing approaches will be needed to overcome these challenges to facilitate the growing interest in recording from the autonomic nervous system.

6.3. Alternative paradigms

Electrical recording remains the dominant modality for recording from the PNS. Whilst neuromodulation via direct electrical stimulation is common (including in clinical applications), few, if any, clinical devices record from peripheral nerves. This is despite a clear need driven by the desire to achieve closed-loop, time-invariant, neural interfaces within the PNS.

There has been some (limited) engagement with active modalities (such as EIT) and with optogenetics [ 131, 132], but conventional electrical recording using either intraneural or extraneural electrodes remains the dominant modality. Many nerve interfaces (such as cuffs) are claimed to be chronically implantable-however, these claims require further verification. Stimulation electrodes need only be placed close to the target nerves, and so a cuff can be placed quite loosely around the nerve. Conversely, recording electrodes need to be placed in direct contact with the epineurium (to maximise recorded signal amplitudes), and so have the potential to cause considerable damage when implanted long-term. There are few [ 26, 48] studies describing the results of chronic implantation of recording electrodes.

At the same time, closed-loop neuromodulation is complicated by the fact that large stimulation artefacts-caused by current flowing from the stimulation electrodes onto the recording electrodes-often saturate the recording amplifiers. Some solutions to this problem exist in the form of either signal processing or electrode balancing, but the problem remains.

A novel approach is to record the magnetic fields produced by the axons rather than the electric field. This would have the benefit of not requiring any direct contact with the nerve, as the magnitude of the observed magnetic field is not related to the contact impedance, instead the location is driven by the SNR required. This concept of magnetoneurography has been demonstrated both ex-vivo and in-vitro. Okawa et al used large arrays of superconducting quantum interference devices to record neural activity transcutaneously from both the brachial plexus and the carpal tunnel area during electrical stimulation [ 133, 134]. In the case of the brachial plexus, it does not appear that any stimulation artefact was present, but in the case of the carpal tunnel area, the authors were forced to use the most distal stimulation site and to employ an artefact rejection tool (common spatial pattern). Barbieri et al performed an in-vitro demonstration of recording from mouse muscle, and also developed a model for the magnetic field contribution from the two axial components in the case of a muscle bundle [ 113]. They recorded using giant-magnetoresistance (GMR) sensors and stimulated with a suction pipette. Despite the proximity of the stimulation electrode and the GMR sensors, they reported no stimulation artefacts.

Interest in recording from the PNS continues to be growing, and as more advanced techniques are developed, it becomes more challenging to know which method to adopt. Thus, this tutorial has introduced a taxonomy of electrode configurations and recording techniques that will support the selection process and can be augmented as new methods are developed. The important aspects of selecting an appropriate animal model, as well the challenges associated with acute and chronic recordings, have been introduced. This tutorial provides a good foundation in peripheral nerve recordings for both the novice and the experienced researcher as the field continues to develop.

No new data were created or analysed in this study.

No funding was provided in support of creating this manuscript.

No new data were created or analysed in this study leading to no ethical protocol requirement.

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Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system

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, , and * Journal of Neural Engineering , Volume 19 , Number 4 Citation Ryan G L Koh DOI 10.1088/1741-2552/ac7d74 et al 2022 J. Neural Eng.

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the ability of peripheral nerve interface (PNI) to inflict as little physiological or histological damage to the tissue as possible. These properties are governed by factors such as the mismatch of mechanical properties between the tissue and the electrode, as well as the immunological reaction of the tissue to different materials and surface treatments.

R. G. L. Koh, J. Zariffa, L. Jabban, S.-C. Yen, N. Donaldson, and B. W. Metcalfe, ‘Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system’, Journal of Neural Engineering, 2022.

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Journal of Neural Engineering , Volume 19 , Number 4 Citation Ryan G L Koh DOI 10.1088/1741-2552/ac7d74 et al 2022 J. Neural Eng. 19 042001

Author affiliations

¹ KITE, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, M5G 2A2, Canada

² Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada

³ Edward S Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M4G 3V9, Canada

⁴ Rehabilitation Sciences Institute, University of Toronto, Toronto, ON M5S 2E4, Canada

⁵ Centre for Biosensors, Bioelectronics, and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom

⁶ Innovation and Design Programme, Faculty of Engineering, National University of Singapore, Singapore, Singapore

⁷ The N.1 Institute for Health, National University of Singapore, Singapore, Singapore

⁸ Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6DH, United Kingdom

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⁹ Author to whom any correspondence should be addressed.

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Abstract

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of PNIs, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

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Neural control is at the heart of our agency in the world. Through a combination of conscious and automatic processes, the nervous system enables us to regulate ourselves and our interactions with our environment. As we seek to modulate, restore, or enhance these abilities, we have turned in recent decades to technologies that can interface effectively with the nervous system. Certain applications benefit from interfaces at the level of the central nervous system (CNS), for example, when dealing with brain disorders such as Parkinson's, psychiatric conditions [ 1, 2] or attempting to coordinate patterns of movement through spinal circuitry [ 3, 4]. In many cases, however, the peripheral nervous system (PNS) offers the advantage of carrying afferent or efferent information that most directly relates to the function of the target of interest.

For most of its history, this field has focused on restoring function lost as a result of amputation [ 5- 7] or paralysis [ 8, 9]. More recently, the scope has broadened considerably (as reviewed in [ 10]), including significant interest in chronic disease applications involving the autonomic nervous system [ 11- 13]. These developments have gone hand-in-hand with new progress in neural interfacing technologies and have spawned the related field of bioelectronic medicine.

Peripheral nerve interfaces (PNIs) can modulate neural activity through stimulation and/or monitor neural control and feedback through recording. Key potential applications of peripheral nerve recordings include the control of prosthetic limbs through extracted motor commands [ 14], the closed-loop control of functional electrical stimulation (FES) systems through extracted afferent information [ 15], and the neuromodulation of body functions through the identification of autonomic control signal [ 16] and electrical disease biomarkers [ 17].

Achieving stable and functional recording in the PNS has been a persistent challenge in neural engineering for several reasons-many of which are distinct from those faced by CNS interfaces. First, in peripheral nerves, the nerve fibres (axons) are tightly packed into fascicles, which are in turn held together by connective tissue to form the nerve trunk [ 18]. The whole structure, in many cases, has a diameter on the order of hundreds of microns to a few millimetres. In this configuration, the axons are densely organised into a relatively small-diameter structure, making it difficult to isolate activity related to a particular function. Second, sparse firing patterns and the lack of large, synchronised populations result in small-amplitude signals. Third, many peripheral nerves experience significant movement during physical activities (e.g. during respiration in larger mammals), and are often located close to muscles whose bioelectrical activity creates substantial interference. Lastly, in the case of chronic implantation, encapsulation tissue can form in and around an electrode and alter the nerve-electrode interface and thus the amplitude and nature of the recorded signals [ 19].

Several device designs have been proposed to create reliable PNIs and these are divided into intraneural and extraneural. The former involves penetration of the device into the nerve trunk, while the latter relies on devices positioned on or near the surface of the trunk [ 20, 21]. These approaches have been considered in a trade-off between selectivity and invasiveness.

Regardless of the approach, multi-channel PNI designs are increasingly providing new possibilities to extract detailed information about the neural function. The availability of multiple channels provides signal processing opportunities for resolving ambiguities that cannot be dealt with in a single channel, and enables powerful machine learning or regression approaches [ 14, 22- 29]. Several recent reviews have covered peripheral nerve electrode designs [ 20, 21, 30, 31], but reviews of the data analysis techniques necessary for the creation of effective PNIs are limited [ 32].

The objective of this tutorial article is to provide a resource that captures the key methods for the analysis of peripheral nerve recordings, and specifically examines the critical interplay between the type of electrode used, the signal analysis techniques applied, and the nature of the information extracted. Further guidance is also given on the importance of appropriate experimental models and the challenges associated with chronic implantation.

The structure of this paper is as follows. Section 2 will introduce general principles and typical approaches to making recordings. Section 3 will introduce the most common electrode geometries and discuss their impact on the nature of information extracted, section 4 will describe the most common signal processing approaches, section 5 will discuss experimental and translational considerations, and section 6 will identify future trends and technologies.

It is important to understand the general principles of a PNI recording system before considering the relative merits afforded by different configurations. The following sections will briefly introduce the properties of peripheral nerve recordings as well as general principles in acquiring and pre-processing these signals.

Figure 1 shows a simplified overview of a typical PNI. Electrodes are placed near, on, or in, the nerve trunk and are connected to low-noise amplifiers (LNAs) and a digital signal processing (DSP) system. The neural signals occurring within the nerve are thus amplified, digitised, and processed to provide an output that can take several forms including: spike trains, continuous time series, and images showing the source of the neural activity within the nerve trunk. This information can then be used to inform a prosthesis such as an artificial limb or a neuromodulation device. It is important first to have an understanding of the nature of neural signals, and so a brief overview will now be given.

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2.1. Biophysics of neural signals

Individual axons produce action currents with magnitudes in the pico-ampere range, due to APs, which may be detected as neural signals. These small action currents give rise to small potentials (on the order of 1 µ V), that are difficult to detect in the presence of noise and interference. There are two ways to obtain a detectable potential: an electrode must be tiny and in close proximity to the axon, so that the potential is produced across the spreading resistance from a node of Ranvier, or there must be a restricted extra-cellular space that creates a high resistance through which the small action currents flow. These two cases give rise to a taxonomy wherein an interface may be defined as operating with either unrestricted (e.g. intraneural) or restricted (e.g. extraneural) extracellular space.

Neural signals that occur spontaneously (i.e. without external stimulation or modulation) are composed of individual APs resulting from normal biological functions. Typically, neurons within different fascicles innervate unrelated tissue, and do not fire synchronously. Consequently, the observed neural signal is characterised by low amplitude and high frequency activity.

Spontaneous signals recorded extraneurally (i.e. within a constrained extracellular space) from the surface of the nerve trunk are rarely larger than 30 µV peak-to-peak [ 33], whereas intraneural signals recorded from inside the trunk can be over 100 µV peak-to-peak [ 34]. Most of the signal power is concentrated in the range of 300 Hz-5 kHz, with the peak below 3 kHz [ 35- 37].

The small amplitude of the neural signal creates significant challenges, especially against the background of instrumentation noise (on the order of 2-4 µ V root-mean-square (RMS)). Adding considerably to this challenge is the interference from nearby muscles (electromyographic (EMG) activity), which can be an order of magnitude larger than the neural signal when both are recorded using a monopolar reference. The EMG bandwidth (approximately 5-500 Hz) has partial overlap with the neural signal, precluding effective removal using linear filtering without loss of information.

Neural signals may also be directly evoked, or modulated, by mechanical, chemical, or electrical stimulation. When this occurs, many axons produce APs simultaneously, and the resulting neural signal (the evoked compound AP (eCAP)) is the result of the superposition of these APs. In this case the amplitude of the eCAP will be much larger than that of a single AP (∼100 µV for cuffs), see figure 6(a) for an exemplar recording of both spontaneous APs and eCAPs.

2.2. Amplification and acquisition

At the amplification and acquisition stage, a well-chosen reference montage can be used to minimise the contributions of interfering sources. The most widespread example of this approach is the tripolar arrangement, in which the signals at the end electrodes are averaged and used as a [ 33]. This configuration helps suppress EMG interference by taking advantage of the linearisation of these electric fields along the length of the recording array [ 38], and can be implemented through several alternative differential recording arrangements [ 39]. For intraneural recordings both monopolar and bipolar approaches have been reported [ 40, 41]. Examples of the monopolar, bipolar, and tripolar amplifier configurations are given in figure 2.

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Acquisition is generally less critical, and can be performed by most good quality analogue-to-digital converters with sufficient sampling rates (typically >30 kHz), although techniques that measure the conduction velocity of the neural signals may require supra-nyquist sample rates to adequately sample fast APs if the inter-electrode distance is small.

2.3. Signal pre-processing and denoising

Noise and interference in neural recordings can arise from a several sources such as interfering muscle activity [ 38], movement artefacts causing audiophonic noise or triboelectric noise, noise from a high impedance ground, or electromagnetic noise [ 26]. Using the appropriate signal processing techniques can help minimise noise and interference.

At the data pre-processing stage, bandpass filtering is commonly applied to isolate the neural signal, with a high-pass frequency in the 250 Hz-1 kHz range and a low-pass frequency in the 3-7.5 kHz range. If the objective is simply to detect the presence of neural activity, a rectified-bin-integration (RBI) approach can be applied, in which windows that contain neural activity, as well as noise, produce higher values than those that only contain noise [ 42]. This approach, however, is accompanied by a loss of temporal resolution that may preclude the use of many of the techniques described in the following sections.

An additional and more sophisticated pre-processing option is wavelet denoising, which relies on transforming the noisy data into an orthogonal time-frequency domain, thresholding the wavelet coefficients to remove the noise, then transforming back to the original time domain. Selection of the mother wavelet and thresholds are key considerations, but reports have varied about which choices are optimal for neural signals [ 35, 41, 43].

The geometry and configuration of the electrodes used to obtain a recording are critical in determining the type of signals that can be measured. Selecting the most appropriate electrode material, structure, configuration, and geometry for a specific application requires an in-depth understanding of the advantages and disadvantages of each. Ciancio et al [ 44] have summarised these requirements, and from their work and the wider literature it is possible to identify two key characteristics of a PNI.

Selectivity-Selectivity refers to the ability of the PNI to stimulate, or record from, specific axons, fascicles, or nerves, whilst being insensitive to off-target axons, fascicles, or nerves. Depending on the application, the desired stimulation selectivity may differ from the recording selectivity; for example, two different electrodes may be used to stimulate and record in the same prosthesis and the overall selectivity may vary from one application [ 7] to another [ 45].

Stability-Equally important is the stability of the PNI, both for acute and chronic applications. It should be stable over time and should inflict as little physiological or histological damage to the tissue as possible [ 46]. These properties are governed by factors such as the mismatch of mechanical properties between the tissue and the electrode, as well as the immunological reaction of the tissue to different materials and surface treatments [ 47].

3.1. Electrode location

3.1.1. Extraneural electrodes

One exception to this is the microchannel interface, in which the nerve trunk may be micro-dissected into fascicles that may then be placed within channels that constrain the extracellular space [ 49]. This results in fewer axons per channel and typically higher signal amplitudes (∼100 µ V).

3.1.2. Intraneural electrodes

Intraneural electrodes are normally implanted directly inside the fascicles, penetrating the perineurium, and show better selectivity than extraneural electrodes as they have closer contact with the fascicles. Examples of these include thin-film longitudinal intrafascicular electrodes (LIFEs) [ 50], and transverse intrafascicular multi-channel electrodes (TIMEs) [ 51]. In terms of stability, their invasiveness is higher than extraneural electrodes and the implantation itself may cause damage [ 21, 52, 53].

3.1.3. Regenerative interfaces

Regenerative interfaces are often designed to interface with small groups of axons, allowing for selective stimulation and recording with the best possible level of selectivity. Instead of penetrating the nerve with the electrode, the nerve is transected and then supported to regrow through a structure containing electrode channels [ 54]. In one embodiment, the structure resembles a sieve consisting of a piece of material with multiple micropores covered with a conductive material. After transection, each end of the nerve is placed on either side of the sieve and the axons grow through the micropores. A schematic representation of this is shown in figure 2.

It has been shown that neurons will regenerate through the sieve structure, and the sieves are functional as both recording and stimulation devices with high selectivity. However, regenerative electrodes may result in incomplete or constrained regeneration of axons, leading to difficulties with chronic implantation [ 55, 56].

3.1.4. Electrode configuration

Several recent papers review the biocompatibility and stability of different electrode materials and structures [ 31, 57]. However, from the point of view of neural recordings, it is helpful to consider a taxonomy of geometries-as it is the spatial relationship between the signal source and the electrodes, alongside the constraining of the extracellular space, that often defines the recording selectivity and capability.

Electrodes may be grouped to form point measurements (single channel), one-dimensional linear arrays (organised circumferentially, transversely, or longitudinally), or two-dimensional linear arrays. Figure 3 illustrates these geometries using an extraneural cuff interface. The substrate (i.e. insulating tube) of the cuff and FINE interfaces constrains the extracellular space and serves to maintain the spatial relationship between the electrodes. Intraneural electrodes may, or may not, have stable fixation and so while the geometries are applicable to all interfaces, the spatial stability should also be considered.

3.2. Point measurements

One-dimensional point measurements are by far the most common and lend themselves to a wide array of signal processing techniques. Point measurements, in this paper, refer to measurements produced from a single recording channel (i.e. an observation at a single spatial location) that may have been referenced by a monopolar, bipolar, or tripolar configuration.

3.3. One-dimensional arrays

One-dimensional arrays can be formed by placing multiple electrodes either longitudinally, transversely, or circumferentially. Thus, both temporal and spatial classification becomes possible.

Temporal classification-relies on the fact that the propagation of the AP may be observed by placing an array of electrodes located longitudinally along the length of the nerve. Different properties of the APs, such as conduction velocity (which is proportional to axon diameter), may then be used to discriminate APs from individual axons, or types of axons [ 58, 59]. These approaches do not require any prior knowledge about the AP morphology, and many can improve the signal-to-noise ratio (SNR) of the recording by averaging over multiple recording channels.

Spatial classification-relies on the fact that an array of electrodes arranged circumferentially can selectively identify activity from within different fascicles or axons, based on the spatial location of each electrode with respect to the neural source. Passive recording approaches include source localisation and types of beamforming, many of which do not require prior knowledge about the expected morphology of the AP but do require high SNRs to localise activity to individual fascicles [ 60].

3.4. Two-dimensional arrays

Circumferentially and longitudinally spaced electrodes can be combined to form a two-dimensional structure that enables the observation of APs in both space and time [ 61].

Spatiotemporal classification-combines the benefits of longitudinal and circumferential arrays for a more comprehensive and robust characterisation of the APs. Classification can be performed using templates, or by training a convolutional neural network (CNN) to recognise the spatiotemporal patterns associated with specific neural activity [ 22].

Section 3 introduced a taxonomy of electrode geometries and explained how the choice of geometry impacts the available signal processing methods-including temporal, spatial and spatiotemporal. Several key analysis techniques will now be introduced and discussed in the context of content extraction. The information of interest in peripheral nerve recordings can be broken down more broadly into two categories: anatomical and functional.

4.1. Anatomical content

Anatomical content pertains to the size, shape, type, and positions of structures within the nerve. In terms of PNI, anatomical information can be provided by making observations of the propagation direction (afferent versus efferent), axon type (e.g. A

Throughout this paper, direction sensitivity refers to the ability to discriminate afferent versus efferent, velocity refers to the conduction velocity of each AP, and location of neural sources refers to the determination of the spatial location of the source of the neural signal within the nerve. Anatomical content is most readily obtained using extraneural electrodes, as they provide a macro view of the activity within the entire nerve, as opposed to intraneural electrodes that offer a more microscopic (and thus spatially localised) view.

4.2. Functional content

Functional content within a nerve pertains to a specific organ or system. For example, signals within the ulnar nerve may encode sensation (via cutaneous afferents) from the hand and forelimb. The overall presence (e.g. a non-selective power measurement) of neural activity can also be considered functional content. Functional interpretation of a signal can be made without knowledge of the anatomical underpinnings (e.g. where exactly in the nerve an axon is located) but does require knowledge of the nerve's innervation.

A common approach to extracting functional content is to identify single unit activity (i.e. APs that result from specific axons) in a process called spike sorting, wherein individual APs are grouped into clusters based on morphology. The resulting single unit labels (spike trains) can then be used directly in a neuroprosthesis without reference to any anatomical organisation. Spike sorting is most used in intraneural electrode configurations wherein the SNR is high and individual APs can be observed. Figure 4 illustrates this process for exemplar data with three active axons.

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More generally, multi-unit single axon AP trains, eCAPs, or signal windows from either intraneural or extraneural recordings can be associated with a particular function of interest through classification or clustering approaches. In this paper the task of associating neural signals with different functional events is termed discrimination of neural pathways.

In some cases, certain information can overlap anatomical and functional content. For example, the location of neural sources refers to the spatial location of the source within the nerve, but with some a priori information, a functional aspect can be determined. In the popular rat sciatic nerve model, identifying neural sources in the tibial nerve would correspond to dorsiflexion of the ankle while identifying neural activity in the peroneal nerve would correspond to plantarflexion of the ankle.

Deciding what information (anatomical vs functional) to obtain depends largely on the application of interest. If the intended use is focused on understanding the underlying physiology, anatomical content will likely be more applicable. On the other hand, functional content may be of more interest if the intended use is to obtain a control signal (i.e. to use in a neuroprosthetic device).

4.3. Content extraction techniques

Once the information of interest is known, the appropriate electrode configuration and data analysis techniques can be employed. Figure 5 introduces a taxonomy of the different methods and the applicable electrode configurations that can be used to determine anatomical or functional content. For example, if one is interested in imaging (producing a cross sectional image with the location of neural sources identified), then a 1D-circumferential cuff could be employed alongside the electrical impedance tomography (EIT).

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4.3.1. Time series analysis

The most common way of analysing neural signals still lies in using time-domain, frequency-domain, or statistical features without necessarily relying on any aspect of the anatomical information (i.e. fibre velocity, spatial location) directly.

Time domain techniques to smooth the neural signal over a window can be used to detect neural activity [ 62]. The most common approach is the RBI operation, where the signal is rectified, binned into time windows, and integrated. This technique extracts the signal's envelope and enables a simpler and smoother signal for analysis compared to the noisier raw recordings. This signal can then be thresholded to determine if a neural source of interest is active.

Another approach for identifying neural activity is to observe the statistical properties of the signal and noise. For example, the autocorrelation matrix of white noise will be diagonal because the samples are completely uncorrelated. Therefore, the eigen-decomposition of this matrix will yield a single non-zero eigenvalue. In contrast, a recording containing both neural data and noise will have off-diagonal elements in the autocorrelation matrix, and thus more than one eigenvalue. The difference between the greatest and smallest eigenvalue can be used to detect neural signals [ 42].

These techniques are simple and effective for detecting neural activity but alone are often inadequate for obtaining more sophisticated functional or anatomical content. However, if the dominant application is to detect neural activity, these techniques are typically sufficient and can be quickly and easily implemented.

4.3.2. Choice of reference

Reference choice has a significant impact on the recording quality of a PNI as it can attenuate interfering signals and can also be used to determine the direction of propagation (i.e. afferent vs efferent signals) without the need for other techniques or methods.

4.3.3. Template matching

The main advantage of template matching is that it is amenable to implementation on an online system, but its effectiveness is reduced if the templates are similar or the number of distinct sources increases. Another drawback occurs when AP shapes overlap in time [ 68] and thus choosing templates that represent the underlying neural activity may not be straightforward. The use of multi-contact electrodes has helped mitigate the effects of this issue.

4.3.4. Velocity selective recording

The concept of velocity discrimination is founded on the fact that the conduction velocity of an AP is a function of the axon properties, all of which are assumed to be either time invariant (e.g. myelin thickness, diameter, membrane properties) or tightly regulated (e.g. temperature, ionic concentrations) [ 69]. The axon diameter and myelin's presence (or lack thereof) are the main factors that determine the difference in conduction velocity from one axon to another.

The velocity of an AP can be computed by delaying the signals recorded from each element of a longitudinal array relative to one another by an interval that corresponds to the conduction velocity before summing together [ 70, 71]. One advantage of this process is the ability to distinguish afferent and efferent neural activity by simply applying a negative delay.

The first demonstrations of velocity selective recording were made using electrical stimulation to recruit large amplitude eCAPs, in worm [ 71] frog, pig, and rat [ 27, 28, 72- 74]. The delay-and-add process is used to provide a spectral representation of eCAP recordings, where the spectrum is presented with respect to velocity rather than frequency. Figure 6(a) shows a time domain eCAP recording and the corresponding velocity spectrum.

4.3.5. Source localisation and beamforming

Most, if not all, techniques for locating neural sources derive from source localisation and beamforming techniques that are related to inverse problems in electroencephalography [ 75]. Briefly, the inverse problem of bioelectric source localisation is based on the equation:

where d is an M ×1 vector containing the recorded data from M electrode contacts, j is an N × 1 vector whose entries represent the magnitude of the current dipoles distributed in the region under consideration, and L, known as the lead field matrix, is an M × N matrix whose entries represents the influence of a unit current dipole on the potential recorded at a particular electrode. is an M × 1 vector of additive noise.

The objective is to recover j based on the measurements of d and estimate of L.

Beamforming is a signal processing technique that uses spatial filters that combine recordings made at different spatial locations to enhance (via constructive interference) the selectivity of the recordings. When tuned correctly, this can be used to localise neural sources within the nerve. The advantage of this technique is that it can provide both anatomical (the location of the neural activity within the nerve) and, with some a priori information, functional content. However, solving the inverse problem is non-trivial and in small nerves the neural sources of interest are much closer in distance, reducing the ability to localise them.

Most source localisation approaches have been attempted with extraneural electrodes [ 25, 26, 29, 76- 82]. An approach using FINE electrodes has shown the most promising results, demonstrating the feasibility of real-time implementation in chronic recordings involving canines [ 26, 82].

4.3.6. Spatiotemporal signatures

The main advantage of this technique comes from the direct integration of the temporal and spatial information allowing for a more comprehensive characterisation of neural activity. These spatiotemporal signatures have been used as an input to a CNN which demonstrated the ability to discriminate naturally evoked CAPs (nCAPs) and be used to reconstruct firing patterns of different neural pathways [ 22]. This network is known as the extraneural spatiotemporal compound AP extraction network or ESCAPE-NET. The overall ESCAPE framework can be seen in figure 7 alongside an example of a spatiotemporal signature.

This technique has shown promising results in an acute rat model. A recent simulation study [ 84] aimed at mimicking chronic conditions suggested that the selectivity of the spatiotemporal signature can be maintained by establishing a recalibration schedule for the classifier.

4.3.7. Electrical impedance tomography (EIT)

EIT is a method that can provide fascicular-level selectivity with an extraneural approach using electrodes distributed around the nerve. EIT is an emerging medical imaging technique in which changes in the impedance of a conductive volume, such as a nerve, may be imaged using an array of external electrodes [ 85, 86]. In this method, a flexible, cylindrical, multi-electrode cuff is placed around a nerve, and the imaging technique of fast EIT is applied to image the activity within the fascicles. Changes in impedance caused by small decreases in bulk tissue resistance occur as ion channels open and close, and these can be detected (with some averaging) using the external electrodes.

Mathematically, EIT is similar in principle to inverse source localisation. However, EIT has significant advantages including: more independent data (for N electrodes, there are O(N ²) independent measurements at a time compared to O(N) for inverse source localisation), a potentially unique solution, no field cancellation problem, and no theoretical limitations on the accuracy [ 87- 89].

The principle of operation is that small currents (typically 30 µA at 6 kHz) are injected between different pairs of electrodes, while the resulting voltage is measured at every other pair. This process is repeated over all electrodes to produce a set of measurements that can be re-constructed into an image of the nerve. Figure 8 illustrates the principle of fast neural EIT in a model peripheral nerve. An impedance change associated with neural activity occurs in figure 8(b) relative to the baseline cases in figures 8(a) and (c). This change in impedance can be observed as a change in the measured voltage of a pair of electrodes to a current applied in another pair of electrodes in figure 8(d). Multiple measurements of this change can be used to produce an image of the activity in figures 8(e) and (h). EIT is an effective recording approach for eCAPs, wherein the averaging process can readily be performed, but has yet to be demonstrated with spontaneous neural signals.

The signal processing and recording methods presented in this paper are given as examples, and new methods will undoubtedly be developed in due course. However, the taxonomy in figure 5 should provide the reader with a powerful tool for selecting the electrode geometry and thus the type of information that can be extracted by the different methods. Having now covered the fundamentals of neural recording and introduced a range of electrode geometries and signal processing methods, the following section will cover the translational considerations and experimental challenges associated with animal models.

The previous sections have introduced the fundamentals of peripheral nerve recordings, from the biophysics of the APs, through the electrode geometry, and then the signal processing. This section will introduce the animal models typically used to develop and test PNIs and discuss the potential translational issues using the context of the cervical vagus nerve as an example target.

5.1. Experimental models

Most of the development of neural interfaces is performed using animal models, in either acute or chronic experiments. There are both ethical and scientific reasons for using animal models; however, these models also lead to several associated translational challenges. A recent detailed review of animal models used for PNI development can be found in Aman et al [ 91].

5.1.1. Acute experiments

In acute surgical experiments, the electrodes (recording, stimulation, or both) are implanted in anesthetised animals and the target nerve is explored over the course of a few hours whilst the animal remains unconscious [ 17, 25, 72, 83, 92, 93]. The animal is then terminated at the end of the experiment. Acute experiments are beneficial because they provide a stable platform for interrogating the nervous system that includes any and all organs or tissues of interest, whilst removing several of the challenges associated with chronic experiments such as movement artefacts and the need for either percutaneous connectors or a wireless telemetry system [ 38].

However, the anaesthetic regime employed for the surgery has an influence on the electrophysiology that remains unclear, even though the molecular mechanisms of anaesthetics are well characterised. For example, it is known that both isoflurane and ketamine, two widely used anaesthetic paradigms, differentially impact sensory processing in the mouse primary visual cortex [ 94]. The complex interactions between the anaesthetic agents, and their compound effect on the electrophysiology of the animal, remain problematic and may hinder the direct translation of results to awake animals.

5.1.2. Chronic experiments

In chronic experiments the electrodes are implanted under general anaesthesia, but the animals are then recovered for a period of time (from days to months) [ 26, 95- 97]. Recording may take place during the implantation surgery, while the animal is awake and freely moving, or during a termination surgery. The former and the latter have the advantage of not requiring percutaneous connectors or a wireless telemetry system but suffer the same drawbacks as acute experiments. Recordings made whilst the animals are awake and freely moving are, for many applications, the most representative but also by far the most challenging. One advantage of using percutaneous connectors is that measurements of the electrode impedances may be made directly, and then related to the recorded signal amplitudes.

It is important that an appropriate species be chosen for the model, based not just on the ease of the experiment but also on any translational opportunities or issues. If the goal of the experiment is to elucidate some fundamental electrophysiologic property or phenomena, then a well-characterised species such as mice or Xenopus Laevis frogs may be suitable. Likewise, the development of electrode materials and the associated instrumentation may not depend greatly on the species and so mice or rats may be chosen based on reasons of cost or simplicity. However, if the goal-at any point in time-is to translate the research into humans, then the choice of model is critical. The difference between the peripheral nerves of humans and other mammals is not obvious; differences may exist in gross anatomy and geometry of the nerves, the type and level of fascicular structure and vascularisation, and the distribution of myelinated versus unmyelinated axons [ 98, 99].

A good example of the difference between species can be found by considering the cervical vagus nerve. The vagus is the tenth cranial nerve and for many years has been the focus of significant interest as a neuromodulation target for the treatment of diseases as broad-ranging as intractable epilepsy [ 100, 101], diabetes [ 102, 103], and rheumatoid arthritis [ 104- 106]. Figure 9 shows the comparative anatomy of the cervical vagus nerve between mouse, rat, canine, non-human primate (NHP), pig and human [ 90]. Each of these will be discussed in the context of the individual animal model.

5.1.3. Rodents and small animals

Rodents, typically mice or rats, have been the staple model for biomedical research for a long time. They are well characterised, easy to care for, and have the benefit of being available in a multitude of different inbred or outbred strains. From an experimental perspective, they can be anaesthetised and maintained without complex anaesthetic equipment. Indeed in rats and mice it is commonplace to use only a single intraperitoneal administration of an agent such as urethane [ 107]. The peripheral nerves are, of course, physically much smaller than in humans. On the one hand this can make the surgical approach to the nerves relatively straightforward, as there is minimal tissue or bone to remove. On the other hand, it makes the design and placement of electrodes far more difficult, thus limiting the number of electrodes that can be placed as well as the separation between stimulation and recording electrodes. The latter point is important as an increased separation between the stimulation and recordings sites is one method for reducing or eliminating stimulation artefacts from recordings.

Animals such as guinea pigs, cats, and rabbits are an attractive middle ground between rodents and larger animals but are much less explored in the literature on electrophysiology. Cats have seen the greatest attention [ 65, 108, 109]. Aside from potential inexperience in working with these species, there is likely to be a stronger perceived ethical concern associated with species that are companion animals. Interestingly, this has led to the implantation of neuromodulation devices in companion animals as a therapy, for example the Brindley device has been implanted in companion dogs to restore urinary bladder control [ 110].

Turning to the cervical vagus as an example target, it is smaller in mice () than in rats (), and typically consists of only 1-2 fascicles (figure 9(a)). The canine vagus is larger again () and has the thickest epineurium across most models, resulting in a greater distance from either the stimulation or recording electrodes placed on the epineural surface to the axons. This in turn would likely alter the stimulation thresholds and the amplitudes of the recorded neural signals. In the case of mice, rats, and canines, the fascicular structure is far less complex than that in humans-further complicating any translation of design parameters from one to the other.

5.1.4. Large animals

Large animals-in this context sheep and pigs-have long been common-place in neuromodulation research, although their prevalence is in part limited by their comparatively complex requirements for both housing and surgical management. Unlike rodents, sheep and pigs are generally not as well characterised, and are typically obtained from commercial farmers or kept in small groups for breeding purposes. Their housing and husbandry requirements are not necessarily more difficult than those of rodents but do require greater space and nutritional resources. Pigs in particular are frequently reported in experimental research and have been for many decades [ 72, 73, 101, 111].

Compared to rodents, the surgical approaches to the peripheral nerves of pigs and sheep can be more complex and time consuming, although the gross anatomy and the approach, in general, have the potential to be more directly translated to humans. Anaesthetic induction and maintenance is complex, and almost always requires a dedicated anaesthetist and mechanical ventilation providing volatile anaesthetics such as sevoflurane or isoflurane [ 72]. Agents such as fentanyl, propofol, and ketamine may be administered before or during surgery, typically as a bolus, although continuous rate infusion of propofol is possible [ 112]. The impact of these agents on the electrophysiology of the animals is more challenging in part due to the number of agents administered.

Returning to the vagus nerve as an example target, it is of similar size () in pigs, sheep, and humans [ 113]. The number of fascicles in the vagus nerve of pigs and sheep is on the order of 30, whereas in humans it is closer to 10. There is a similar thickness of epineurium and level of vascularisation across the three species. Thus, pigs and sheep represent appropriate models for developing electrodes, instrumentation, and surgical approaches that might be translated into humans. The distance between the axons and electrodes placed on the epineurium is likely to be similar, so stimulation thresholds and recorded signal amplitudes ought to be comparable.

5.1.5. Humans and non-human primates (NHPs)

Experiments involving either humans or NHPs are the least common, due in large part to the ethical and regulatory issues surrounding them. In humans, most experiments are performed as part of a larger rehabilitation package wherein the patient is implanted with a therapeutic device that may also collect data, or as part of a clinical trial for an innovative technology. Good examples of this include PNIs that are used for either controlling upper-limb prosthesis or for proving sensory feedback from a prosthesis-such experimental interfaces have been implanted in humans for several weeks or months [ 6, 7, 114, 115].

NHPs have predominantly been used in experiments that target the CNS, wherein single unit recordings can be made from awake, behaving animals using high-density electrode arrays [ 116]. The surgical approaches used in NHPs are most like those used in humans and other large mammals, an attribute that both aids potential clinical translation but also raises the costs and complexities of experimentation. One significant benefit with most NHPs is that they can be trained to perform purposive motor tasks (such as reaching and grasping), and this has made them popular in various areas of sensorimotor research, for example in the development of PNIs for the control of prosthetic limbs [ 117] or the use of optogenetic modulation to improve motor functionality [ 118]. However, there is often significant training time (and thus cost) associated with the development of purposive experimental paradigms [ 91].

Returning to the vagus nerve as an example target, the vagus nerve in NHPs is smaller than that of the human () with notably less complex fascicular organisation [ 113]. The number of fascicles in the vagus nerve of NHPs is typically only 1-2, but there is a similar (relative) thickness of epineurium and level of vascularisation. Both the marked differences in vagus nerve diameter and fascicular organisation make it challenging to appropriately design a translatable interface for this nerve, although the NHP model can be valuable for the testing of chronic implant stability. From both an economic and ethical standpoint, NHPs should only be used when development of an interface is sufficiently advanced so that the benefits of this model, like complex movement behaviour and similarity to human biomechanics, are scientifically mandatory.

5.2. Chronic recordings

There are several points during a chronic experiment when neural recordings can be made: during implantation surgery, whilst awake and freely moving, and during termination surgery. There is intrinsic value in each of these, and many breakthroughs in the chronic stability of the neural interfaces have been made without recording from an awake animal-see for example the recording of bladder activity in rats [ 49]. However, it is often the goal to record neural signals whilst the subject is awake and freely moving. The challenges associated with doing this are multiple and span from husbandry and care to electrical interference and noise.

Fundamentally, the recording system needs to measure the potentials at the electrodes and communicate this to a remote device for processing or logging. The most straightforward way of achieving this is via percutaneous connectors; implanted cables are routed from the electrodes to the connector, and external instrumentation can then be connected on an ad-hoc basis [ 26]. Challenges include healing of the wound surrounding the connector, risk of damage due to normal or abnormal animal behaviour, and mounting the connector at a suitable anatomic site. Electrical issues can arise from the length of the cable that may be required between the electrodes and the amplifier, or from triboelectric effects from the cables themselves [ 26].

Implanting the instrumentation is an attractive alternative to percutaneous connectors, for example using a telemeter [ 119]. In this scenario, the instrumentation, potentially including a power source and a wireless communication system, is packaged in an implantable device that is located near the recording electrodes. Data storage is likely to be limited, and in most cases a receiving device must be placed on the skin over the site of the telemeter to enable data collection.

Finally, there are fundamental challenges associated with the movement of the animal. Modern amplifiers are available with very high common-mode rejection ratios (CMRRs), and so in theory, should be more than capable of rejecting common-mode signals from nearby muscles. In practice, however, differential electrode impedances and the filter networks that might be placed before the amplifiers, will degrade the CMRR. Thus, in recordings from awake and freely moving animals, it remains very challenging to ensure that recordings are not contaminated by interference from nearby muscles.

6.1. Closed-loop interfaces

Information extracted from peripheral nerve activity can be used to improve functional or health outcomes. This relationship typically takes the form of closed-loop stimulation, where the timing, pattern and/or location of stimulation are continuously adjusted based on the measured neural activity. The translational success of peripheral nerve recording techniques is therefore closely tied to closed-loop neuromodulation systems. Several studies in animal models have demonstrated the control of FES based on limb position or state estimates derived from peripheral nerve recordings, using both extraneural [ 120, 121] and intraneural [ 122] recordings. Early examples in humans included the regulation of grasping strength in a hand neuroprosthesis based on volar digital nerve feedback [ 8], and foot-drop correction using sural nerve recordings [ 123], both using nerve cuff recordings.

More recent work in humans demonstrated decoding of motor intention combined with sensory stimulation, using slanted microelectrode arrays [ 14]. While most of these efforts focused on neuroprosthetic applications, a study by Plachta et al is noteworthy for demonstrating an application related to bioelectronics medicine, namely the control of blood pressure using selective recording and stimulation of the vagus nerve [ 16].

Despite these examples, the number of studies that have demonstrated closed-loop stimulation based on peripheral trunk recordings remains low, although a substantial number of studies cite this concept as their motivation. This gap can be partially attributed to the challenges with obtaining informative and stable peripheral nerve recordings. Another notable challenge is the need for strategies for removing stimulation artefacts to effectively coordinate recording and stimulation. Artefact removal in this context may require a combination of hardware (e.g. blanking systems to prevent amplifier saturation during stimulation) and signal processing components [ 124], which increases the complexity of the instrumentation required and contributes to the low number of studies. Nonetheless, the variety of techniques described in this article and the recent acceleration of the field suggest that we are at a turning point in this regard and artefact rejection signal processing schemes that demonstrate a 25-40 dB rejection in the artefact are now available [ 125].

6.2. Autonomic nerve applications

Most of the techniques described in this manuscript have been developed for sensorimotor applications, such as the control of prosthetic limbs. However, there is a growing interest in the ability to record from smaller fibres within the autonomic nervous system (such fibres are frequently of interest in pain research). Recent advancements in hardware (e.g. more compact electrodes and multiple electrode contacts) have led to opportunities for recording from these small autonomic nerves. Thinly myelinated and un-myelinated axons (e.g. C-fibres) conduct APs more slowly (<2 m s −1) than larger fibres, and the resulting AP and CAP amplitudes are much smaller. Thus, it is challenging to record C-fibre activity without employing a very selective interface (e.g. micro-needles [ 126]) or using supra-maximal stimulation to recruit all of the C-fibres simultaneously and thus maximise the amplitude of the resulting eCAP.

Microneurography, being the easiest way to identify single C-fibre APs, employs a needle that is able to record single or compound APs [ 127]. This approach allows for unambiguous recording and recognition of C-fibre APs. Microneurography is beneficial for developing a better mechanistic understanding of peripheral nerve encoding and modulation but is not suitable in most cases for chronic use.

Novel electrode designs have also been developed for the purposes of recording from small-fibre nerves, but novel signal processing techniques for obtaining small-fibre activity are limited [ 128- 130]. This is mainly because validation of small-fibre activity is extremely challenging due to the low signal amplitude and the slow conduction velocity. Velocity selective recording (VSR) may be of significant benefit in this application as the enhancement of the SNR of the recorded signal will maximise the likelihood of observing APs from small-fibre activity, and both the conduction velocity and the direction can be validated. After locating these small-fibre APs using VSR, template matching and spatiotemporal signatures may be able to separate activities of interest, but no studies using signal processing techniques have shown this possibility.

Interfacing with these slower-conducting nerve fibres will require new or adapted recording and analysis methods. These techniques will need to either enhance the SNRs of the recorded signals to address the low signal amplitudes or be able to isolate small-fibre activity. It is likely that a combination of novel electrode design and signal processing approaches will be needed to overcome these challenges to facilitate the growing interest in recording from the autonomic nervous system.

6.3. Alternative paradigms

Electrical recording remains the dominant modality for recording from the PNS. Whilst neuromodulation via direct electrical stimulation is common (including in clinical applications), few, if any, clinical devices record from peripheral nerves. This is despite a clear need driven by the desire to achieve closed-loop, time-invariant, neural interfaces within the PNS.

There has been some (limited) engagement with active modalities (such as EIT) and with optogenetics [ 131, 132], but conventional electrical recording using either intraneural or extraneural electrodes remains the dominant modality. Many nerve interfaces (such as cuffs) are claimed to be chronically implantable-however, these claims require further verification. Stimulation electrodes need only be placed close to the target nerves, and so a cuff can be placed quite loosely around the nerve. Conversely, recording electrodes need to be placed in direct contact with the epineurium (to maximise recorded signal amplitudes), and so have the potential to cause considerable damage when implanted long-term. There are few [ 26, 48] studies describing the results of chronic implantation of recording electrodes.

At the same time, closed-loop neuromodulation is complicated by the fact that large stimulation artefacts-caused by current flowing from the stimulation electrodes onto the recording electrodes-often saturate the recording amplifiers. Some solutions to this problem exist in the form of either signal processing or electrode balancing, but the problem remains.

A novel approach is to record the magnetic fields produced by the axons rather than the electric field. This would have the benefit of not requiring any direct contact with the nerve, as the magnitude of the observed magnetic field is not related to the contact impedance, instead the location is driven by the SNR required. This concept of magnetoneurography has been demonstrated both ex-vivo and in-vitro. Okawa et al used large arrays of superconducting quantum interference devices to record neural activity transcutaneously from both the brachial plexus and the carpal tunnel area during electrical stimulation [ 133, 134]. In the case of the brachial plexus, it does not appear that any stimulation artefact was present, but in the case of the carpal tunnel area, the authors were forced to use the most distal stimulation site and to employ an artefact rejection tool (common spatial pattern). Barbieri et al performed an in-vitro demonstration of recording from mouse muscle, and also developed a model for the magnetic field contribution from the two axial components in the case of a muscle bundle [ 113]. They recorded using giant-magnetoresistance (GMR) sensors and stimulated with a suction pipette. Despite the proximity of the stimulation electrode and the GMR sensors, they reported no stimulation artefacts.

Interest in recording from the PNS continues to be growing, and as more advanced techniques are developed, it becomes more challenging to know which method to adopt. Thus, this tutorial has introduced a taxonomy of electrode configurations and recording techniques that will support the selection process and can be augmented as new methods are developed. The important aspects of selecting an appropriate animal model, as well the challenges associated with acute and chronic recordings, have been introduced. This tutorial provides a good foundation in peripheral nerve recordings for both the novice and the experienced researcher as the field continues to develop.

No new data were created or analysed in this study.

No funding was provided in support of creating this manuscript.

No new data were created or analysed in this study leading to no ethical protocol requirement.

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Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system

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, , and * Journal of Neural Engineering , Volume 19 , Number 4 Citation Ryan G L Koh DOI 10.1088/1741-2552/ac7d74 et al 2022 J. Neural Eng.

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is a method that can provide fascicular-level selectivity with an extraneural approach using electrodes distributed around the nerve. EIT is an emerging medical imaging technique in which changes in the impedance of a conductive volume, such as a nerve, may be imaged using an array of external electrodes. In this method, a flexible, cylindrical, multi-electrode cuff is placed around a nerve, and the imaging technique of fast EIT is applied to image the activity within the fascicles. Changes in impedance caused by small decreases in bulk tissue resistance occur as ion channels open and close, and these can be detected (with some averaging) using the external electrodes.

R. G. L. Koh, J. Zariffa, L. Jabban, S.-C. Yen, N. Donaldson, and B. W. Metcalfe, ‘Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system’, Journal of Neural Engineering, 2022.

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Laminectomy is surgery that creates space by removing bone spurs and tissues associated with arthritis of the spine. It usually involves removing a small piece of the back part (lamina) of the small bones of the spine (vertebrae).

N. E. Epstein, ‘Laminectomy for cervical myelopathy’, Spinal cord, vol. 41, no. 6, pp. 317–327, 2003.

https://www.nature.com/articles/3101477

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Cervical laminectomy remains a useful operative alternative for the management of cervical myelopathy where the cervical lordotic curvature is well-preserved. This is particularly true in the geriatric age group where comorbidities increase operative risk. Cervical laminectomy may address congenital or acquired stenosis, multilevel spondylosis, select cases of ossification of the posterior longitudinal ligament (OPLL), and ossification of the yellow ligament (OYL). Chronic olisthy or frank instability may be selectively and simultaneously addressed with varied posterior stabilization procedures.

Anatomy and pathophysiology

Absolute stenosis is defined by a canal measuring 10 mm in anterior/posterior dimension, while relative stenosis denotes a 13 mm canal. Both radicular and myelopathic complaints arise when the canal is narrowed to these critical dimensions as the cord itself occupies between 0.8 and 1.3 cm in the anterior/posterior dimension, and soft tissues take up another 2-3 mm. Disc herniations, spurs, OPLL, and OYL further compromise the already narrowed space, increasing the likelihood of incurring a significant neurological deficit even with minor traumatic events especially extension. Direct mechanical trauma and indirect ischemia contribute to neurological dysfunction. Gray and white matter damage associated with cord atrophy, demyelination, and infarction are often located several segments above or below the site of maximal cord compromise.

Clinical parameters and neurological grading

Patients with absolute cervical stenosis develop radicular or myelopathic complaints in their 40s-50s, while those with relative stenosis present more typically in their 50s and 60s. Patients with lumbar stenosis over 65 years of age should routinely have cervical studies performed as 10% of these individuals will have significant tandem cervical stenosis. For these patients with myeloradiculopathy, male usually outnumber female patients - 2:1. Radicular complaints include pain, numbness, tingling, or weakness in a specific or varied dermatomal distribution. Myelopathy, usually painless, is characterized by upper and lower extremity weakness, numbness, and loss of balance.

Myelopathy is evaluated with several grading systems: Nurick Grades, Japanese Orthopedic Association Score, and Ranawat Scores. The Nurick Grades are as follows: Grade 0 - intact, Grade 1 - mild myelopathy, Grade II - mild-to-moderate myelopathy, Grade III - moderate myelopathy, Grade IV - moderate-to-severe myelopathy, and Grade V - severe myelopathy. The Japanese Orthopedic Association Score assigns 17 points to upper and lower extremity function. The Ranawat System, devised particularly for those with rheumatoid arthritis, employs four grades, Grades I, II, IIIA, and III B, the latter reflecting severe quadriparesis or quadriplegia.

Preoperative myelopathic deficits

Patients undergoing cervical laminectomy usually exhibit moderate-to-severe preoperative myelopathy (Nurick Grades III-IV). This is characterized by atrophy of the thenar and hypothenar eminence, stereoanesthesia, the 'useless hand syndrome', and proximal lower extremity weakness. Other findings include diffuse hyperreflexia accompanied by bilateral Babinski responses, a relative level to pin prick from C5 downward with loss of vibratory and position appreciation with the inability to walk in a tandem fashion.

Differentiation from neurodegenerative disorders

Neurodegenerative disorders that must be differentiated from cervical spondylotic myelopathy or OPLL-related myelopathy include multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), paraneoplastic syndromes, and toxic-metabolic disorders (diabetes, alcoholism, vitamin B ₁₂ deficiency). Magnetic resonance imaging scans, cerebrospinal fluid studies, serum studies, peripheral nerve or muscle biopsies, and electromyographic evaluations help determine whether these syndromes are present. Other entities such as diffuse idiopathic skeletal hyperostosis (DISH) may also contribute to or coexist with OPLL-related myelopathy.

Biomechanics of laminectomy

Flexion and extension maneuvers alter the space available to the spinal cord and nerve roots. Flexion, by increasing axial tension, narrows the AP diameter of the cord, while extension shortens and thickens the cord dimensions. Hyperextension in a congenitally stenotic spinal canal with hyperlordosis, results in inward shingling of the laminae and infolding of hypertrophied or ossified yellow ligament both of which may further contribute to significant dorsal cord compression. Laminectomy in this setting immediately increases the space available for the spinal cord, and results in improved cord perfusion as axial tension is released.

Biomechanics of facet resection

Differing degrees of facet resection may contribute to instability following laminectomy. Typically, stability is maintained following resection of less than 25% of the medial aspect of the facet joint. However, 50, 75 and 100% of facet joint resection results in successively greater degrees of instability as confirmed in both biomechanical and clinical studies.

Neurodiagnostic studies

Cervical stenosis may be determined through 'Gestalt' techniques or by using direct measurements on plain X-ray alone. If the anterior margin of the facet joints overlaps the interlaminar line, the canal is congenitally stenotic. If the vertebral body in its anteroposterior dimension cannot 'fit' within the spinal canal, the canal is congenitally narrowed. Alternatively, one may directly calculate the anterior-posterior measurements of the vertebral canal, and create a ratio of the two confirming the finding of stenosis.

Dynamic X-rays

Dynamic X-rays help define whether patients have sufficient lordosis to undergo laminectomy for stenosis successfully. On static or dynamic films, a minimal lordotic curvature of 10° is required if laminectomy is to be performed, which sufficiently allows the cord to migrate away from ventrally situated osteophytes or OPLL. Candidates for laminectomy or laminoplasty should exhibit at least 10° of lordosis and less than 7 mm of ventral OPLL or spondylosis. Where laminoplasty is to be considered, a minimum of 14 mm of anterior-posterior decompression should be feasible for those with CSM compared with a greater 17 mm for those with OPLL.

Frank instability is defined on flexion/extension studies by more than 3.5 mm of subluxation, greater than 20° of angulation, and over 1-2 mm of motion demonstrated between the tips of adjacent spinous processes.

MR

MR readily defines spinal cord and nerve root compression as well as other intrinsic cord abnormalities both preoperatively and postoperatively in multiple planes. Cerebrospinal fluid remains hypointense on T1-weighted studies, while the cord appears isointense. T2-weighted studies create a 'myelographic' image as CSF becomes hyperintense, while the cord itself stays hypointense. Increased cord signals on T2 images indicate demyelination, edema, or myelomalacia, and constitute poorer prognostic findings for patients with spondylotic myelopathy compared with those exhibiting OPLL. Those with severe, irretrievable cord damage should not be considered surgical candidates as their chances for recovery are minimal.

Shortcomings of MR include the inability to visualize bony pathology directly. Bone will appear hypointense, creating a negative image, often underestimating the true extent of cervical spondylosis or OPLL.

Dynamic MR

Dynamic MR studies may further document spinal cord compression not readily visualized on static studies. Preoperatively, ventral compression from spurs or OPLL and dorsal intrusion from shingled laminae, infolded hypertrophied or ossified yellow ligament, accompanied by instability/olisthy may be seen. Following laminectomy, dynamic MR scans may demonstrate foci of residual cord compromise, reversal of the lordosis, or kyphosis not appreciated on static studies. Employing dynamic MR, Morimoto et al found that 5 years postlaminectomy, a 37-year-old male required a C4-C7 anterior corpectomy and fusion for further progression of OPLL intruding on the cord in flexion studies.

CT

CT directly documents the calcification, ossification, and configuration of cervical bony pathology. CT, myelo-CT, 2D- and 3D-CT studies demonstrate in multiple planes, the conformation of the cervical spinal canal ( Figure 1 and Figure 2). In particular, the transaxial myelo-CT provides better detail regarding foraminal pathology than the MR. However, if the cord is severely compromised and the increased signal in the cord corresponds with active cord compression rather than long-standing myelomalacia, a myelo-CT may be contraindicated as this may risk acute neurological deterioration. Rather, 2D- or 3D-CT studies should be employed.

Conservative treatment of cervical stenosis

Management of mild cervical stenosis or even more severe stenosis in the elderly who are not considered surgical candidates is performed usually by neurologists and pain center specialists. Treatment modalities include cervical collars, short-term steroidal and long-term non-steroid anti-inflammatory medications, physical therapy, biofeedback techniques, and occasional epidural steroid injections. Occasional patients with mild myelopathy, despite MR scans showing increased cord signals, may be successfully managed with various traction methods and cervical bracing techniques.

Preventative cervical decompression

There are few critical indications for considering pro-phylactic cervical surgery. Patients under 65 years of age, if mildly symptomatic or at risk for quadriplegia with even mild trauma, may warrant early decompression. However, for patients 65-70 years of age, increased comorbid factors substantially increase the risk of prophylactic decompression. Poor prognostic factors found in multiple studies include advanced age (>70 years old), cardiac disease, peripheral vascular disease, obstructive pulmonary disease, diabetes, stroke, hypertension, and alcoholism. Older individuals with these comorbid factors, if considered for surgery, re-quire preoperative diagnostic studies that may include nuclear stress testing, pulmonary function studies, carotid and/or arterial lower extremity doppler studies, and others. Patients on anticoagulation or on antiplatelet aggregants because of mechanical cardiac valves, a history of TIA or stroke, unstable atrial fibrillation, active myo-cardial or peripheral vascular arterial or venous disease must be encouraged to seek nonsurgical alternatives for the management of their cervical myelopathic syndromes.

Anesthesia

Nasotracheal or endotracheal fiberoptic intubation, awake positioning, and continuous intraoperative somatosensory-evoked potential monitoring are employed for patients with significant cervical cord compression undergoing laminectomy. With the patient immobilized in a hard cervical collar, Q-tips coated with neosynephrine are first placed in the nasopharynx through the larger nares. The nares is successively dilated with larger nasal trumpets coated with 0.5% Xylocaine Jelly. Prior to fiberoptic placement of the endotracheal tube, a transtracheal injection of 5 cm ³ of injectable 1% Lidocaine HCL may also be utilized. If an endotracheal procedure is performed, the patient may suck on a tongue blade coated with 1% Lidocaine Jelly or gargle with aerosolized lidocaine. Next, a fiberoptic cable passed through a wire-reinforced anode tube (ie 7.5 mm) is threaded through the vocal cords under direct vision. The balloon is inflated just above the carina and the tube is taped in situ. Lidocaine 0.5% with epinephrine 1:200,000 is then injected (5 cm ³ per injection) at the three pin sites of the Mayfield head holder. With the hard collar in place, the patient is then turned prone. For the next 5 min in the prone posture, the neurological examination is checked, while SSEPS are studied to make sure that baselines have been maintained. Undue flexion or extension in the three-pin-head holder or poor operative positioning of the shoulders, arms, or hands may result in SSEP decrement or loss; correction of any one of these problems prior to induction is critical. Arterial line monitoring is used to maintain the blood pressure usually 20% above the norm, avoiding hypo-tension. A foley catheter is placed to monitor urinary output. Patients are also prophylactically administered 1 gm of intravenous methylprednisolone for cord 'protection'.

Monitoring of cervical surgery

Intraoperative somatosensory-evoked potential monitoring includes the noninvasive assessment of median or ulnar, and posterior tibial function throughout surgery. Significant changes are defined by a greater than 50% loss of amplitude or greater than 10% prolongation of latency. Medical resuscitative measures that address deterioration include: increased oxygenation, decreased inhalation or intravenous anesthetic, warming of irrigating solutions, induction of hypertension, and the addition of peroxide. Surgical resuscitative measure include: the release of distraction, removal of an over-sized graft resulting in undue cord traction, and cessation of manipulation. Note that false positives are treated as 'mini-true positives' requiring adjustment of technique and the institution of medical or surgical resuscitative maneuvers. Motor-evoked potentials may also be employed intraoperatively in addition to SSEPs as they help to monitor anterior cord function directly. Intraoperative electromyographic monitoring (EMG) is employed during focal root manipulation as well as during any stabilization procedure.

Operative positioning

The pros of using the sitting position for laminectomy include reduced intraoperative bleeding particularly in obese patients with short thick necks, easier intraoperative ventilation for those with pulmonary disease, and greater accessibility for cardiac massage should cardiac arrest occur. The cons of the seated posture may outweigh the pros, including the greater risk of air embolism, hypotension, stroke, and cord ischemia, particularly in older individuals with significant cardiovascular pathology.

Laminectomy

The laminectomy is performed by injecting the skin and paracervical musculature with 0.5% Bupivicaine HCL with 1:200,000 epinephrine (30 cm ³). A midline incision is effected between the C2 and T1 levels. Subperiosteal dissection of the ligamentum nuchae and the paracervical muscles from the spinous processes and laminae of C3-C7 is accomplished, sparing the muscular attachments to C2 and T1. To avoid denervation of the erector spinae muscles, the retractors should be intermittently released. A laminectomy usually extends two levels above and below the site of maximal cord compression. Bilateral troughs are created medial to the facet joints on either side after removing 1/2 of the respective spinous processes ( Figure 3 and Figure 4). The outer cortex of the laminae is removed while the inner cortex is thinned to 1-2 mm with sequentially slower drills employing 4 mm diamond bits only. Removal of the remaining 1-2 mm may be accomplished with a 1-2 mm Kerrison punch or the diamond drill if hypertrophied yellow ligament is visualized directly underlying the bone to be removed. This helps avoid inadvertent perforation of an often atretic dura. Medial facetectomy (25%) and foraminotomy are accomplished bilaterally utilizing a 45° angled, 2 mm rotating Kerrison rongeur. Undecut-ting of the adjacent C2 and T1 laminae is also performed to ensure midline cephalad and caudad decompression.

Lateral disc herniation or spur removal is performed by introducing a small nerve hook parallel to the thecal sac to identify the exiting nerve root. Next, a small down-biting curette may excise the sequestrated fragment or be used to cut down the spur. EMG monitoring during this portion of the operation helps warn of impending root injury in the course of dissection.

Postoperatively, a hard cervical collar is employed for up to 6 weeks following a cervical laminectomy, while a cervicothoracic orthosis may be employed in patients undergoing posterior stabilization until CT documents fusion. Postoperative MR studies readily demonstrate adequate cord decompression along with atrophic changes within the cord ( Figure 5).

Laminoforaminotomy

Laminoforaminotomy alone or in combination with laminectomy may be utilized to approach spondylotic spur formation or herniated discs. Zeidman and Ducker performed 172 of these procedures, and demonstrated the efficacy of the laminoforaminotomy for the management of monoradicular syndromes without fusion. When performed at the cervico-thoracic junction, such procedures may warrant simultaneous fusion. More recently, Adamson's experience with 100 microendoscopic posterior cervical laminoforaminotomies observed excellent to good results in 97 patients with few complications being observed.

Dentate section

The dentate ligaments transmit cephalocaudal axial stresses between the spinal cord and dura, and do not 'tether' the cord either ventrally or dorsally as popularly believed. Therefore, intradural section of dentate ligaments will not promote cord decompression, but will unnecessarily subject patients to the risks of pneumocephalus, subdural and epidural hematoma formation, postoperative CSF fistulas, meningitis, and chronic adhesive arachnoiditis.

Posterior stabilization

Posterior stabilization, either performed primarily, or secondarily to address instability following failed anterior or posterior procedures, has long employed different laminar, spinous process, and facet wiring techniques. For patients sustaining cervical spine trauma, 100% fusion rates were achieved using Bohlman's triple wire technique. For the management of pseudoarthrosis following failed anterior procedures, posterior spinous process wiring and fusion resulted in a 94% fusion rate compared to only a 76% fusion rate for anterior reoperations.

Facet fusions may prevent or treat instability following laminectomy. Fusion occurred in 79% of Miyazaki et al's 46 patients following laminectomy, where noninstrumented facet fusions were performed. Postlaminectomy, Luque rectangle-facet fusion was successful in 90% of cases compared with a 96% fusion rate following facet wiring with a braided titanium cable. With lateral mass plates, 83% of Abumi and Kaneda's patients undergoing laminectomy demonstrated fusion without kyphosis. Postlaminectomy fusion rates employing lateral mass plates approached 100% with limited morbidity, a 6.1% incidence of screw malposition or pullout, a 1.8% frequency of nerve root injury, and a low incidence of lateral mass fractures.

Facet wiring and fusion

Bilateral facet wiring and fusion provides adequate stabilization following cervical laminectomy ( Figures 6, 7 and 8). Facet joint wiring requires drilling out the articular cartilage, removing one-fourth of the superior articular facet, and careful preservation of the inferior articular facet so that the wire will not pull through as it is passed. Once the inferior articular facet is isolated, a right angle 1 mm dental drill is used to make a hole extending into the facet joint. A braided titanium cable is passed through this hole and may either be used to wire facet to facet in a continuous fashion or to individually wire at each level to a fibula strut allograft, which sits longitudinally along the laminectomy defect ( Figure 8). Before the wire top hat is crimped, autologous bone is packed into the bare facet joint and supplemented with demineralized bone matrix.

Lateral mass plates

Lateral mass constructs, now using either bicortical or unicortical screws, are affixed to plates or more adjustable rod systems. The long-term efficacy of cervical laminectomy accompanied by posterior lateral mass plate placement was evaluated by Kumar et al using the Short-Form 36 questionnaire: 80% of patients experienced good outcomes, and 76% demonstrated improvement in myelopathy scores. Lateral mass plates have been applied with a relatively low complication rate. Of 654 screws placed in one series, complications included nerve root injury in 0.6%, facet violation in 0.2%, broken screws in 0.3%, screw avulsion in 0.2%, and screw loosening in 1.1%. Other complications observed included cord injury (2.6%), iatrogenic foraminal stenosis (2.6%), broken plates (1.3%), loss of reduction (2.6%), adjacent segment degeneration (3.8%), infection (1.3%), and pseudarthrosis (1.4%).

Pedicle screw fixation

Pedicle screw fixation techniques offer another alternative for fusion following laminectomy. When Abumi et al, used a one-stage posterior decompression and reconstructive technique for the management of 46 patients with CSM and OPLL, 5.3% of screws perforated the cortex of the pedicles risking neurovascular injury.

In the cervical spine, neither lateral mass or pedicle screw techniques are Federal Drug Administration (FDA) approved in the United States. For example, the disclaimer issued with the Vertex system (Sofamor Danek, Memphis, TN, USA) specifically limits its use to the upper thoracic spine (T1-T3). Despite the lack of FDA approval, these devices are commonly used by many spine surgeons who consider these devices to be part of the 'standard of care'. Nevertheless, when a suit is brought against a spine surgeon for utilizing this device, the lack of FDA approval serves as a 'red flag', which markedly increases the likelihood of a plaintiff's verdict and size of the award or settlement. Presently, there is a medical malpractice crisis in the United States where neurosurgeons pay up to $160,000-$250,000 for coverage, and rates for orthopedists continue to rise. Therefore, although a spine surgeon may prefer to utilize lateral mass or pedicle screw techniques for posterior cervical fusions, they markedly increase their malpractice exposure and susceptibility to successful plaintiff's litigation in the event of an untoward result.

Complications of laminectomy

Spinal cord and nerve root injuries vary from 0% to beyond 10%. Yonenobu et al, observed a 5.5% incidence of new cord and nerve root injuries (predominantly involving the C5 root) following 204 anterior procedures, 85 laminectomies, and 95 laminoplasties. Rapid cord migration following anterior or posterior surgery rather than direct operative trauma may contribute to immediate or delayed radicular deficits. Looking at the frequency of postoperative radiculopathy following 287 cervical laminectomies, Dai et al, observed that 37 (12.9%) patients exhibited new postoperative root deficits involving the C5 and C6 nerve roots. To avoid such deficits following anterior cervical procedures, Saunders et al recommended limiting the ventral troughs to 15 mm in diameter.

Postlaminectomy kyphosis in Kaptain et al series was observed 21% of the time, but did not correlate with postoperative deterioration. Progressive kyphosis, following laminectomy observed in 47% of the patients in Kato et al series, also did not correlate with more adverse neurological outcomes.

However, when calculating the morbidity of performing laminectomy with fusion versus laminoplasty following dorsal decompressions, Heller et al, observed a lower complication rate and higher degree of functional improvement for laminoplasty. Nevertheless, although matched cohorts were employed to perform this study, the presence of only 13 patients in each treatment group limits the validity of the conclusions.

Management of Durotomy

Cerebrospinal dural tears may be directly sutured or stapled with microdural staplers. Additionally dural grafts and muscular flaps may be applied, followed by the placement of graft matrix and fibrin sealant.

Outcomes following laminectomy

Myelopathic individuals over 65 years of age do well in the short-term but not long-term following laminectomy. Ebersold et al, found that acute postoperative neurological improvement rates were comparable following both laminectomy (68%) and anterior surgical procedures (72%). Even short-term reoperation rates were similar after laminectomy (13.7%) and anterior surgery (12%). However, the incidence of delayed deterioration (average 7.35 years postoperatively) was significantly higher following laminectomy (10%) used more often in older individuals with high comorbid status as compared with anterior surgery 2%. Similar findings were recounted in Kato et al series where patients, followed an average of 14 years following laminectomy, exhibited a 23% incidence of delayed deterioration. Although 1-year (44%) and 5-year recovery rates (43%) were maintained, showing no evidence of short-term deterioration, significant worsening occurred between 5 and 10 years postoperatively (33%). Factors correlated with increased deterioration included age over 70 at the time of the first surgery, severe original myelopathy, and recent trauma.

Laminectomy for central cord syndrome

The role for delayed laminectomy to treat central cord syndromes in stenotic patients remains unproven. When Levi et al performed laminectomy 17 days following trauma-related central cord syndromes, only 12 of 20 patients improved, while seven were unchanged and one deteriorated.

Laminoplasty

Some consider laminoplasty an alternative to laminectomy or laminectomy with posterior stabilization. The typical laminoplasty involves removal of the ventral and dorsal cortex of the lamina on one side just medial to the facet joints, while on the opposite side, only the dorsal cortex is removed. A 'green-stick' fracture based on the hinged side is then completed, allowing the lamina to be elevated away from the underlying cord. Various techniques have subsequently evolved to 'keep the door open'. When Kohno et al performed laminoplasty for cervical spondylotic myelopathy and/or OPLL, they noted that 18 of 22 patients improved. Poor prognostic factors in this and other studies included age over 60-70 years, long symptom duration (>18 months), trauma, severe cord compression, kyphosis, a high signal in the cord on T2-weighted MR scans, cord atrophy, and sphincteric complaints. Biomechanical evaluation in laminoplasty versus laminectomy in a goat model, Baisden et al, found that laminoplasty was superior to laminectomy in avoiding postoperative deformity.

Outcomes following laminoplasty, laminectomy and anterior surgery

Dorsal decompression of any type correlates with more rapid progression of OPLL. Comparing laminoplasty (25 patients) and laminectomy (16 patients) patients with those treated without surgery (56 patient), Takatsu et al, found that OPLL grew more rapidly in the dorsally operated groups. Patients undergoing anterior surgery showed greater neurological recovery than those having laminectomy or laminoplasty. Kawano et al, noted a 78% improvement rate following anterior surgery (14 patients) versus a lower 46% improvement rate after laminoplasty or laminectomy (61 patients).

Anterior corpectomy with fusion and posterior stabilization for OPLL

Multilevel anterior corpectomy with fusion and posterior stabilization procedures performed without plates, with fixed plates, and newly introduced dynamic plates, readily address multisegmental OPLL in younger patients. In Epstein's original series of 22 non-plated severely myelopathic patients, circumferential surgery resulted in significant neurological improvement (+3 Nurick Grades), but three acute graft extrusions required emergent reoperations. Subsequently, 22 patients with similar pathology had a fixed plate/fixed screw system applied during comparable circumferential operations; they too markedly improved neurologically, although two exhibited subacute (6 weeks) plate-related failures with one patient ultimately requiring plate removal 6 months later for persistent dysphagia. Another 16 patients having a fixed plate/variable screw system applied during these multilevel constructs showed marked resolution of myelopathy, but three developed subacute (6 weeks) plate extrusions requiring urgent reoperations. More recently, 26 patients undergoing multilevel circumferential operations employing dynamic ABC plates (Aesculap, Tuttlingen, Germany) have improved to a Nurick Grades 0-I status (radiculopathy, mild myelopathy), with 25 successfully fusing, and only one showing a delayed pseudarthrosis warranting repeated posterior fusion.

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Laminectomy for cervical myelopathy - Spinal Cord

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Study design: Cervical laminectomy with or without fusion, or laminoplasty, successfully address congenital or acquired stenosis, multilevel spondylosis, ossification of the posterior longitudinal ligament (OPLL), and ossification of the yellow ligament (OYL). To optimize surgical results, however, these procedures should be applied to carefully selected patients.

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fascicle-specific targeting (FAST) technique. 

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fascicle-specific targeting (FAST) technique. 

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https://journals.physiology.org/doi/full/10.1152/jn.00933.2017

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https://onlinelibrary.wiley.com/doi/10.1002/mus.10149

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An engram is a unit of cognitive information imprinted in a physical substance, theorized to be the means by which memories are stored as biophysical or biochemical changes in the brain or other biological tissue, in response to external stimuli.

https://www.sciencedirect.com/science/article/pii/S0896627317304567#bib20

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The Body Model Theory of Somatosensory Cortex

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I outline a microcircuit theory of somatosensory cortex as a body model serving both for body representation and "body simulation." A modular model of...