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This class has been very eye opening to me, learning more about the use of AI in the classroom and the use of Brain Control Interface. As I walk away from this class, I am walking away with many different ideas of how I can more efficiently utilize technology in my teaching. As a seasoned teacher, I did not grow up with the technology that our student's today have. I do well with incorporating technology and it's many uses is not instinctual to me like it is for so many. This class gave me further insight into programs that I can be using to not only create resources, but programs that will help supplement student learning. My hope is that the more I use the resources provided throughout this class and the more experience I get with technology, the more instinctual it will become.

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Final Assignment: Animal Research Presentation using Adobe Express

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Final Assignment: Animal Research Presentation using Adobe Express

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Student Centered Lesson Using Adobe Express

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Check out this Presentation designed by Kristina Spillers.

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Reflection 7: Ethics and AI in the classroom

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Reflection 7: Ethics and AI in the classroom

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Part 1

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Part 2

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This publication provides AI guidance for teachers and schools and the use of AI in the classroom.

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[Shared] AI Guidance for Schools Toolkit_TeachAI

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This publication talks about using AI for research in education and the ethics involved.

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Part 2

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Part 3, final video

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Reflection 5: Brain Control Interface Part 2

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Reflection 5: Brain Control Interface Part 2

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Part 2

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Reflection 4: Incorporating STEAM into the classroom

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Reflection 4: Incorporating STEAM into the classroom

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As a reading teacher, incorporating STEAM into the classroom is a lot of fun! Studies show that incorporating STEAM into reading helps keep students engaged and improves reading comprehension skills. The use of STEAM also helps our students develop that critical background knowledge they need to become more strategic readers. Whenever I have the opportunity, I love to incorporate read a louds and accompanying STEAM activities into my classroom. A unit I have not yet implemented, but eventually hope to teach, is on the book, The Wild Robot by Peter Brown. Prior to reading the book, students will be tasked with researching robots and creating a short presentation on robots and how they are currently being utilized in society. As a class, we would then read and discuss the book. Throughout the book, Roz the robot, shows resourcefulness by learning from and adapting to his surroundings. My students would be tasked with using their own resourcefulness by bringing in recyclable items from home. Such as tin cans, cardboard boxes, plastic milk jugs, water bottles, etc. In groups of 3 or 4 they will be challenged with planning for and building a robot that can stand independently, using only the recyclables they brought in from home. Upon completing the robot, each group will name their robot and develop a purpose for the robot. Each student will then be tasked with writing their own creative writing essay about the robot, incorporating the robot's purpose and research from the pre-reading research project.

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Getting Started with Microsoft Sway

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Sway: Create and share interactive reports, presentations, personal stories, and more.

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Sway is an easy-to-use digital storytelling app for creating interactive reports, presentations, personal stories and more. Its built-in design engine helps you create professional designs in minutes. With Sway, your images, text, videos, and other multimedia all flow together in a way that enhances your story. Sway makes sure your creations look great on any screen.

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This website is developed by NASA and includes STEAM activities for students and has a variety of STEM resources for educators.

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Find fun and interesting hands-on activities, games, and more for kids and students, grades K-4 all the way to college.

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The idea of a sophisticated, artificially intelligent program that chooses just the right digital content for a student, at just the right time, can home in on students' strengths and weaknesses and can support or push them is on the educational horizon.

Some companies already claim their ed-tech products do just that-or some version of it-to customize learning. But are AI and personalized learning really the "dynamic duo" that some educators are hoping for?

Andreas Oranje, the general manager of research in research and development at the Educational Testing Service, says the time is right to examine how these technologies are evolving and the implications for K-12 teaching and learning. Educators are right to be excited about the potential of these technologies. But he cautions that educators should also be wary of the potential dark sides around how artificial intelligence is applied and used.

When people talk about artificial intelligence in the education field, they're often referring to machine learning, Oranje said, in which a machine is trained to perform some teaching tasks, typically more quickly and on a larger scale than the human brain can do. But artificial intelligence has the potential to do much more, because true AI technologies are in a state of continuous learning, developing better strategies and tactics as they analyze more data. But as these systems take in more data, they can also become more biased, depending on the input.

Oranje spoke with Education Week about the perils and promise of AI in personalized learning.

How should we be thinking about the way artificial intelligence fits into the concept of personalized learning?

There are three ways to think about personalized learning and how machine learning or AI can take on a different role. The most obvious is around guiding learning activities. You do a learning activity, there's some assessment or continuous evaluation, and some kind of algorithm says, "This is what you should do next." It could be around addressing weaknesses, it could be around fortifying strengths and expanding on those, or it could be completing some domain.

The second form is more on the cusp of adaptive and personalized learning. Algorithms guide learning through scaffolding and responsive assessment. If a student struggles with something, there may be hints that pop up, additional tools like dictionaries, calculators, things like that. There's immediate evaluation, sort of the stealth assessment of where the student is, what they're doing, and how the program can help.

The third one is more about feedback. Everyone goes through the same activity or learning experience, but there are different levels of feedback for different students based on where they are. For example, someone who is really struggling with getting sentences together and paragraph writing, you want feedback that focuses on grammar, usage, spelling, and style-the mechanics. Someone who is more advanced, you want to give feedback about discourse or argumentation style.

How will AI change the role of the teacher around personalized learning?

It will mostly be about data streams that come to the teacher and the teacher delegating some decisions to a system and some not. It's about increased nudges and helps and supports that the learner gets during their learning-look at this, have you considered this?-a lot of the feedback adaptation. I can also see teachers being much more like coaches who spend more time on the difficult, complex problems with smaller groups and individual students. AI technology will afford them the time to do this because a lot of that learning is managed through a system.

As we create a more complex world and look at a broader set of competencies and domains, from social-emotional learning to cross-cultural competency to collaborative problem solving, I can see the teachers focusing more on those aspects. The domains that are better understood are then more relegated to learning systems. I can see a situation where we empower teachers with more data and more evidence about students' performance and how they feel and where they are and where they're coming from to give them a broader and deeper learning experience.

So what does the AI technology actually do to help a student and a teacher personalize the learning process?

Some of that is about automation and some of it is machine learning. A lot of it gets lumped together. Being able to elucidate and aggregate data at scale. Technology and some machine-learning algorithms may predict what students may struggle with; that kind of information can help teachers know where to go next. It's usually very specific and targeted applications that are a success. Applications that are very broad about predicting scores are usually not as successful as things that are very focused on specific learning objectives.

Where we see this stuff mostly being used so far is in the domain of mathematics, where you have these progressive, clean, discrete topics you work on. It's much more specific as opposed to reading or writing, where it's much harder to predict anything.

Should teachers be worried that AI is going to replace them?

It's no surprise that people, including teachers, are fearful about AI taking over jobs, especially if you look at how it's sometimes portrayed in the media. And, it is certainly true that certain tasks will be automated and that jobs that contain mostly or entirely tasks that follow predictable patterns will be at risk. However, teaching is a very complex profession and AI will not be able to automate that much. In fact, I predict that it will lead to an expansion of education, not contraction. Between some automation of standard tasks, but also increased access to education for more people and deeper insights in learning through analytics, I think that the demand for teachers will go up. And, they will be asked more and more to do what they are uniquely qualified to do: instruct, coach, mentor, differentiate, individualize, and inspire.

You've raised concerns about how AI programs can go wrong. What are the pitfalls here?

It's very worrisome. I did a presentation at ISTE [International Society for Technology in Education] that focused on how we can arm teachers with pointed and important questions to [reassure] themselves that whatever is offered is reasonable and works for them. Even if good data has been collected, they may collect data for one population and one purpose that may have nothing to do with what one specific teacher is using it for. Those kinds of things can create biases and can even hamper learning for some students because they're being classified or put in a bucket where they don't belong or that doesn't apply to them. Those are some of the concerns that are most salient.

But if AI is so smart, why can't it prevent those things from happening?

The thing with AI and machine learning is that these things are still very much human decisions. We're encoding human decisions into a system, so human prejudices and biases are simply encoded into the system. The only difference is that the systems can be applied much more at scale, which means our biases can get exacerbated. There's not always a good emergency brake to say, "Not so fast, have you really tested this out for this population, do you really know what it's doing?"

How do you see teachers working with an AI system cooperatively to help make sure students are getting a personalized-learning experience?

You want teachers to help guide these algorithms, review things, be able to press on the emergency brake when they clearly know the student is ready for this and the algorithm says something completely different. They can intervene. But also you want these algorithms to help teachers see around their own biases-"hey, your students might be ready for this," and I didn't think that as a teacher, so I'm going to try this. It works both ways. It's that, hand-in-hand, teacher with tools that is a very powerful and good thing.

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Q&A: The Promise and Pitfalls of Artificial Intelligence and Personalized Learning

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The idea of a sophisticated, artificially intelligent program that chooses just the right digital content for a student, at just the right time, can home in on students' strengths and weaknesses and can support or push them is on the educational horizon. Some companies already claim their ed-tech products do just that-or some version of it-to customize learning.

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As I reflect upon the information provided in the resource, CoSN Driving K-12 Innovation, what stands out to me are the shifts occurring in education following the COVID-19 pandemic. When the pandemic hit, schools were scrambling to provide meaningful education digitally. Upon returning to schools, the shift toward digitizing learning has occurred. However, it is not enough just to provide the needed technology. Our pedagogy in the classroom needs to continue to shift in order to ensure meaningful learning. Prior to the pandemic, the focus in education was more on memorization. With the emergence of AI and other Instructional Technology, our focus needs to shift from memorization towards critical thinking, creativity and collaboration.

Living in a very rural area, another concern that arises is digital equity and inadequate broadband access. So many of our families have limited internet or access to the needed technology. Thus affecting their ability to access all learning opportunities . For schools, just providing the needed technology is not sufficient if internet access is unavailable.

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What Works, What Doesn’t, and How to Tell: The Data That Should Drive K–12 Edtech Decisions in 2025-26

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District leaders, now more than in years past, are less interested in vendor promises and more focused on evidence that learning took place.

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This article focuses on the switch to technology following the pandemic and how our pedagogy in the classroom needs to continue to evolve to meet the ever-changing needs of our students.

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Rewiring the classroom: How the COVID-19 pandemic transformed K-12 education | Brookings

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Brian A. Jacob and Cristina Stanojevich explore new challenges and opportunities for students, educators, and policymakers post-pandemic.

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Introduction

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This is a website for BrainGate. This is a group of engineers, doctors, and Scientists testing the use of technology for people with disabilities. It takes a look at the technologies that can assist them, more specifically the use of Brain Control Interface.

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A consortium of clinicians, scientists, and engineers developing brain-computer interfaces to restore movement and communication for people with paralysis

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Resource Reflection 5: Brain Control Interface

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Resource Reflection 5: Brain Control Interface

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This article takes a deeper dive into how BCI works and the progress made on this technology since 2016.

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Reflection 3 Resources: The Use of Scree Time

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Reflection 3 Resources: The Use of Scree Time

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Medically reviewed by Jennifer F. Cross, M.D. Share Facebook LinkedIn

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What Does Too Much Screen Time Do to Children’s Brains? | Health Matters | NYP

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Child behavioral expert Dr. Jennifer Cross explains why screen time should be limited for kids and why some screens are better than others.

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Reflection 3 Resources: The use of Screen Time

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Reflection 3 Resources: The use of Screen Time

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Thomas Robinson, MD, professor of pediatrics and of medicine, has been studying the effect of watching mobile and video screens since the early 1990s. But before your thoughts go to Tamagachis and Gameboys, Robinson took an interest in screens for their potential to support (and undermine) one's health.

"Even back then, well before smartphones, children were already spending more time watching screens than they were spending in school or doing any other single activity," Robinson said. "Adults were watching even more."

Robinson suspected that even if the daily effects on health and well-being are relatively small, over the course of months and years, those hours accumulate to something that has a major impact on health and development. As a pediatrician, Robinson believes it's crucial to understand the effects of such a major activity in the lives of children and families.

In the following Q&A, Robinson discusses the controversies over screen time and usage, how screens affect health, and his approach to studying such a complicated topic, among other matters.
Your research prioritizes approaches that yield solutions, something you've dubbed the "solution-oriented research paradigm." What does that mean? Can you give an example?
A lot of research is focused on discovering causes, risk factors and correlates of diseases, but it does not directly tell us how to prevent or treat them - that is, solutions. Changing how one frames research questions and designs studies can get to solutions more directly. For example, in our early screen media research, it was hypothesized that screen time caused obesity, aggression and other problems.

Many epidemiological studies, including our own, found relatively weak associations between screen time and these outcomes but they were limited by the difficulty of measuring screen time accurately. The media industry, arguing that the studies did not show cause and effect, blocked practice and policy initiatives. But even if one could prove, with experimental studies, that screen viewing caused health problems, the findings still wouldn't tell us what to do about it.

Instead, we designed behavioral interventions to help reduce children's screen time, and we tested the approaches in a series of randomized controlled trials to observe the effects. We showed that children who receive our interventions to reduce screen time - which helped the children stay under weekly screen time budgets - gained less weight, were less aggressive to their peers and asked their parents to buy them fewer advertised products, compared with kids who did not receive the intervention.

We didn't have to first prove the ill effects of screen viewing - we were able to prove the benefits of an intervention to reduce screen time, directly discovering a solution that could be applied.
There's quite a bit of controversy about how screens affect kids - what do we know and what are the big unknowns still to decipher?
It seems obvious that screens, particularly smartphones, are affecting kids - and adults. The list of suspected effects is long: It is not unusual to hear digital media blamed for everything from mental and physical health problems to impaired brain development and social skills to the downfall of democracy, but it's also credited with improving lives by helping kids stay safe and in touch with distant family members. Many of these impacts are backed up with plausible mechanisms and observations.

Unfortunately, after literally thousands of studies, we have very few clear answers. Some of this stems from problems of measuring screen use and of differences among children and how they use screens. Those are problems we are trying to help solve with our Human Screenome Project. But what we know from studies of screens before smartphones, such as television and video games, still holds. Kids learn behaviors - such as aggression and gender and racial stereotypes - from what they see portrayed in media. The time spent on screens can displace time spent doing other activities, such as time with family, playing outside and schoolwork. And while we're waiting for research to catch up, we can base our parenting, school and public policies on what we know from research on earlier media, along with the impacts that seem obvious to us.
Tell me about the Human Screenome Project, and what you aim to do with it.
With my colleagues Byron Reeves, professor of communication, and Nilam Ram, professor of communication and of psychology, and many other collaborators across campus, we are trying to address some of the limitations of past media research by measuring exactly what people are seeing and doing on their screens.

Consented volunteer participants use an app on their devices that unobtrusively records a screenshot every few seconds, encrypts the image and accompanying data and securely sends it to our research servers. Machine learning and analytics software identify what's on each screen - all the words, images and other screen characteristics. We called it the screenome, like the genome, because it creates a record of individuals' unique experiences on their screens. It feels like what it must have felt like to invent the electron microscope or MRI, to be able to observe a part of life that has been, up until now, invisible.

To date, we have collected screenomes and accompanying surveys for up to one year from several hundred adults and adolescents and their parents. It immediately became apparent that no two people share similar screenomes and that no person's two days or two hours in a day are the same. Our goals for this project are to obtain the first detailed understanding of how people are using their screen devices and what they are seeing; to test relationships between the many hypothesized health, psychological and social effects of screen media and specific screen exposures and behaviors, such as social media, misinformation or jumping from screen to screen; and to design and test precision interventions based on specific moment-to-moment screen experiences.
What advice do you have to help parents manage their child's screen use, especially mobile phones?
For infants and young children, I recommend avoiding screens altogether. Young children benefit from more face-to-face time with their parents and other family members. That means it is also important for parents to limit their own screen use when they are with their children. I also recommend delaying children's use of screens as they grow.

We recently published a study that followed kids from as young as 7 to as old as 15 as they received their first phone. We found, on average, getting a phone at a younger age was no better or worse than getting it at an older age, in terms of depressive symptoms, sleep and school grades. Although some children experienced negative changes after getting a phone, the age of receiving a phone did not appear to be a key factor in determining these outcomes. It is possible that parents are matching their decisions about when to give their child a phone with their child's readiness and their child's and family's needs.

I recommend parents wait to give their child a phone until they are mature enough to regulate their own use and not allow it to distract them from sleep, homework, family time, and playing and socializing with other kids in the real world. Parents must also be aware that once their child has a phone they may be exposed to cyber bullying, body shaming, discrimination and racism, violence, pornography, predatory advertising and other content they may not want their child to experience.

I also suggest families have rules about screen use, such as limiting kids' time on screens, limiting the shows they are allowed to watch and apps they are allowed to use, creating phone-free activities (like spending time with family, eating meals, doing homework and sleeping) and creating phone-free spaces, like bedrooms. There are parental controls and apps to help parents enact some of these limits. And, I tell parents it's OK to take away a child's phone if they do not demonstrate sufficient responsibility to follow the rules.

Lastly, instead of simple screen ownership and screen time, we believe the key is how children and adults use their phones - what they see and do on their phones. We are studying this now in our Human Screenome Project.

Photo by Farknot Architect

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Screen time: The good, the healthy and the mind-numbing

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Researchers at Stanford Medicine are exploring the impact of screen time and how to create solutions that support health.

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This article takes a look at the ethical issues of Brain Control Interface.

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Ethical aspects of brain computer interfaces: a scoping review - BMC Medical Ethics

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Background Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e.g., communication). The unprecedented direct connection created by BCI between human brains and computer hardware raises various ethical, social, and legal challenges that merit further examination and discussion. Methods To identify and characterize the key issues associated with BCI use, we performed a scoping review of biomedical ethics literature, analyzing the ethics concerns cited across multiple disciplines, including philosophy and medicine. Results Based on this investigation, we report that BCI research and its potential translation to therapeutic intervention generate significant ethical, legal, and social concerns, notably with regards to personhood, stigma, autonomy, privacy, research ethics, safety, responsibility, and justice. Our review of the literature determined, furthermore, that while these issues have been enumerated extensively, few concrete recommendations have been expressed. Conclusions We conclude that future research should focus on remedying a lack of practical solutions to the ethical challenges of BCI, alongside the collection of empirical data on the perspectives of the public, BCI users, and BCI researchers.

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This article reviews how BCI can be used with individuals with disabilities and the positive effect this technology can have on the quality of their life.

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REVIEW article

Front. Hum. Neurosci. , 10 February 2026

Sec. Brain-Computer Interfaces

Volume 20 - 2026 | https://doi.org/10.3389/fnhum.2026.1738876

Table 1

Key studies on brain computer interface (BCI)-based assistive and rehabilitative technologies (ARTs) for people with disabilities.
View in article

Table 2

Key characteristics of neuroimaging techniques and their applicability in BCI applications.
View in article

REVIEW article

Front. Hum. Neurosci. , 10 February 2026

Sec. Brain-Computer Interfaces

Volume 20 - 2026 | https://doi.org/10.3389/fnhum.2026.1738876

Abstract

Brain-computer interfaces (BCIs) facilitate functional interaction between the brain and external devices, enabling users to bypass their typical peripheral motor actions to control assistive and rehabilitative technologies (ARTs). This review critically evaluates the state-of-the-art BCI-based ARTs by integrating the psychosocial and health-related factors impacting user needs, highlighting the influence of brain changes during development and aging on the design and ethical use of BCI technologies. As direct human-computer interfaces, BCI-based ARTs offer extended degrees of freedom via augmented mobility, cognition and communication, especially to people with disabilities. However, the innovation in BCI-based ARTs is guided by the complexity of disability types and levels of function across users that define individual needs. Therefore, an adaptable design is essential for tailoring a BCI-based ART that can fulfill user-specific requirements, which may hinder the scalability of BCIs for their widespread adoption across users with disabilities. The trade-offs between implantable and non-implantable BCIs are explored along with complex decisions around informed consent for people with communication or cognitive disabilities and pediatric settings. Non-implantable BCIs offer broader accessibility and transferability across users due to wider standardized signal acquisition and algorithm generalization, making them suited for a more comprehensive user group. This review contributes to the field by providing individualized user needs-informed discussion of BCI-based ARTs, emphasizing the need for adaptable designs that align the evolving functional and developmental needs of users with disabilities.

1 Introduction

Neurological disorders such as stroke, spinal cord injury, amyotrophic lateral sclerosis, attention deficit hyperactivity disorder, Alzheimer's disease, Parkinson's disease, and cerebral palsy can lead to functional disabilities and often involve associated impairments that affect communication, cognition and mobility, ultimately impacting quality of life ( Metzger et al., 2023; Bekteshi et al., 2023; Hallett et al., 2022; Monforte et al., 2021; Makris et al., 2021; Micera et al., 2020; Goulet et al., 2019; Milekovic et al., 2018; Kasahara et al., 2018; Goldstein and Abrahams, 2013; Lim et al., 2012; Liberati et al., 2012). Multiple factors, from personal and environmental contexts to social and welfare elements, impact people with disabilities and their ability to participate in everyday life. The International Classification of Functioning, Disability and Health (ICF), developed by the World Health Organization ( 2002, 2007) (WHO), provides a comprehensive biopsychosocial model for understanding disability. It categorizes functioning across three domains: body functions and structures, activities and participation, and environmental and personal factors. This framework applies universally across all health conditions and emphasizes the interaction between health conditions and contextual factors in shaping individual functioning. Figure 1 schematically illustrates the complex interplay between the diverse psychosocial and health-related factors for assessing diverse health conditions, such as neurological conditions causing disabilities ( Andresen et al., 2016; Hill et al., 2015; Mohammadi et al., 2023; Zhu et al., 2022; Edughele et al., 2022; Stamford et al., 2015). Neurological conditions can affect individuals in anatomical, physiological, psychological and cognitive domains, causing different impairments. The type and severity of impairments characterize the residual functional abilities of people with disabilities to participate in daily life activities. Diversity in impairments and disabilities combined with environmental and personal factors shapes highly individualized user needs, promoting the necessity of user-centric design of brain-computer interface (BCI)-based assisitive and rehabilitative technologies (ARTs) that fulfils user needs. Considering the psychosocial and health-related factors that influence individual needs is critical. Studies investigated users' basic characteristics such as gender and age, and aspects of operating BCI in naturalistic environments such as living situation, caregiver support and insurance coverage and their influence on BCI performance and usability ( von Groll et al., 2024; Fry et al., 2022; Kögel et al., 2020; Ahn and Jun, 2015). All these considerations are critical for translating BCI-based ARTs from laboratory to commercial-grade use, providing widespread access to people with disabilities.

1.1 Scope of the review

This narrative review provides a broad yet critical evaluation of the state-of-the-art BCI-based ARTs, employing a flexible method for reviewing and selecting the literature. Research databases, including CINAHL, Embase, Engineering Village, IEEE Xplore Digital Library, MEDLINE, and PubMed, were utilized to search for relevant literature. Publications addressing the current state of BCI-based ARTs were selected and included in this review. Search terms were used related to: (1) individualization of BCI; (2) user-centric design strategies for ARTs; and (3) to fulfil the evolving functional and developmental needs of users with disabilities.

This review contributes to the field by providing a holistic overview of BCI-based ARTs in terms of user-centric design philosophy to fulfil individualized user requirements of people with disabilities. Studies have investigated the importance of an inclusive, personalized approach for the development of communication BCI for children and adults with disabilities, not only due to inter-individual variability, but also due to the importance of including opinions from all stakeholders ( Shrivastava et al., 2025; Branco et al., 2025; Russo et al., 2025; French et al., 2024; Ivanov et al., 2023; Branco et al., 2023). ( Edelman et al. 2024) have recently evaluated current advancements in noninvasive BCI technologies. At the same time, other reviews have focused on invasive options ( Dunlap et al., 2020; Ferguson et al., 2019). In contrast, this review compares both invasive and noninvasive neuroimaging techniques for their suitability in diverse ARTs for people with disabilities, with a perspective on user needs. ( Patrick-Krueger et al. 2024) have explored the current state of clinical trials of implantable BCIs. Some reviews have investigated different digital signal processing, machine learning, and artificial intelligence techniques, as well as BCI decoding algorithms ( Saha and Baumert, 2020; Azab et al., 2018; Lotte et al., 2018; Jayaram et al., 2016; Krusienski et al., 2011; Bashashati et al., 2007; Lotte et al., 2007). ( Saha et al. 2021) published a review on BCI advancements, discussing the diverse application areas of BCI in general, unlike this review, which addresses individualized applications for people with disabilities. On the contrary, most, if not all, state-of-the-art reviews aim to consider a particular user group or a specific challenge or opportunity in terms of neuroimaging, signal processing, pattern recognition, clinical and socioeconomic aspects of BCI development ( Patrick-Krueger et al., 2024; Edelman et al., 2024; Bergeron et al., 2023; Kaongoen et al., 2023; Karikari and Koshechkin, 2023; Lopez-Bernal et al., 2022; Karlsson et al., 2022; Keser et al., 2022; Fry et al., 2022; Gao et al., 2022; Hramov et al., 2021; Mrachacz-Kersting et al., 2021; Orlandi et al., 2021; Dunlap et al., 2020; Duun-Henriksen et al., 2020; Martini et al., 2020; Micera et al., 2020; Saha and Baumert, 2020; Ferguson et al., 2019; Azab et al., 2018; Deffieux et al., 2018; Lotte et al., 2018; Jayaram et al., 2016; Klein and Ojemann, 2016; Krusienski et al., 2011; Bashashati et al., 2007; Lotte et al., 2007; Dobkin, 2007).

2 Brain-computer interface for people with disabilities

A BCI facilitates functional interaction between the brain and a computer, enabling the decoding and encoding of neural information from and into the brain ( Card et al., 2024; Willett et al., 2023; Huggins et al., 2022; Karlsson et al., 2022; Orlandi et al., 2021; Mrachacz-Kersting et al., 2021; Willett et al., 2021; Saha et al., 2021; Moses et al., 2021; Flesher et al., 2021; Mrachacz-Kersting et al., 2016; Rao et al., 2014; Moghimi et al., 2013). It provides novel ways of communication for end-users intending to interact with their surroundings through ARTs. Figure 2 illustrates a simplified block diagram of a bidirectional BCI and its application in diverse ARTs for people with disabilities. Neural decoding utilizes implantable or non-implantable neuroimaging modalities to record electrical and hemodynamic responses of the brain that corresponds to a user's intentions or cognitive states ( Liu et al., 2025; Chen et al., 2025; Saha et al., 2021; Moghimi et al., 2013; Patrick-Krueger et al., 2024; Edelman et al., 2024; Karikari and Koshechkin, 2023; Martini et al., 2020; Min et al., 2010). The captured brain signals are classified through digital signal processing and pattern recognition algorithms ( Lotte et al., 2018; Krusienski et al., 2011; Bashashati et al., 2007; Lotte et al., 2007) to use them to operate different types of ARTs, for example, wheelchairs, prosthetic arms, virtual reality, and neurostimulation modalities ( Mrachacz-Kersting et al., 2021; Saha et al., 2021; Flesher et al., 2021; Mrachacz-Kersting et al., 2016; Cao et al., 2014).

Other assistive technologies, such as eye-tracking and voice-controlled devices ( Sunny et al., 2021; Tran et al., 2020), may also offer assistance for users than BCI-based ARTs. However, there are circumstances when BCI outperforms other existing ART interfaces. A BCI typically exploits residual brain functions explicitly without any peripheral muscular input, and this is critical for people with severe disabilities post-neurological incidents ( Willett et al., 2023; Moses et al., 2021). Users with limited or no functional abilities may not operate an eye-tracking or voice-controlled device. By utilizing residual brain functions, BCI-based ARTs can provide effective rehabilitation interventions or alternative communication links for users to interact with their surroundings. Thus, the appropriateness of BCI-based ARTs highly depends on individual circumstances. Nonetheless, the rapid evolution of BCI technologies in recent years makes them suitable for manifold applications for diverse user cohorts ( Edelman et al., 2024; Karikari and Koshechkin, 2023; Lopez-Bernal et al., 2022; Karlsson et al., 2022; Saha et al., 2021). Table 1 summarizes some state-of-the-art original research studies on BCI-based ARTs for people with diverse types of disabilities.

References

User group

Disability addressed

Signal type

Key contribution

Branco et al. (2025)

Parents and caregivers of children or young adults with cerebral palsy

Gross motor function classification system scores (I-V)

Motor or speech imagination and visual or auditory evoked potential

Emphasizing a personalized approach for the development of communication BCI for children and young adult with quadrepgic cerebral palsy due to inter-subject variable characteristics

Key studies on brain computer interface (BCI)-based assistive and rehabilitative technologies (ARTs) for people with disabilities.

2.1 Necessity of user-centric design

The practical usability of a BCI depends on various factors, primarily the user-centric design of ARTs ( Shrivastava et al., 2025; Russo et al., 2025; Branco et al., 2025; French et al., 2024; Ivanov et al., 2023; Kübler et al., 2014). The user-centric design is an iterative approach that includes users' perspectives while developing BCI-based ARTs to fulfil individualized user needs. The ICF framework aligns closely with user-centric design principles in BCI development. By recognizing the influence of environmental and personal factors, such as caregiver support, living conditions, and user preferences, the ICF supports the creation of adaptable technologies that reflect real-world diversity in user needs and contexts. It provides a holistic method to evaluate the usability of ARTs for people with disabilities. Some studies proposed user-centric design-based techniques for assessing usability of BCI applications in real-life settings, going beyond developer-centric evaluation criteria such as classification accuracy and information transfer rate ( Ivanov et al., 2023; Kübler et al., 2014). Other studies conducted surveys on people with disabilities and their caregivers seeking their opinions on BCI-based ARTs ( Shrivastava et al., 2025; Russo et al., 2025). Overall, BCIs are acceptable; however, the usability will depend on how developers integrate the input from the users in the development of BCI-based ARTs.

The clinical distinction of neurological variability defines disability types and promotes individualization of BCI-based ARTs. While one group of users encounters developmental disabilities like people with cerebral palsy, others experience acquired disabilities due to neurological incidents such as stroke, amyotrophic lateral sclerosis, and spinal cord injury ( Metzger et al., 2023; Bekteshi et al., 2023; Hallett et al., 2022; Monforte et al., 2021; Makris et al., 2021; Micera et al., 2020; Goulet et al., 2019; Milekovic et al., 2018; Goldstein and Abrahams, 2013). Due to the diverse types and severities of impairments that reflect individualized user needs ( Seghier and Price, 2018; Park et al., 2016), the user-centric and personalized design of BCI-based ARTs may better fulfil the individualized requirements of people with disabilities ( French et al., 2024; Branco et al., 2023; Saha et al., 2021; Martin et al., 2018).

2.2 Assistive and rehabilitative technologies

The principles of BCI-based assistive or communication technologies rely on how brain signals are translated into machine commands, for example, using two distinct brain activity patterns to control a switch (on/off) ( Cao et al., 2014; Mitchell et al., 2023). These can take active, reactive and passive forms of translation through a BCI. The active BCI translates users' intentions, such as imagined movements and speech, into control signals or messages on a computer screen utilizing sophisticated decoding algorithms and software ( Willett et al., 2023, 2021; Saha et al., 2021; Moses et al., 2021; Acı et al., 2019; Aricò et al., 2018; Zander et al., 2009). However, the success of these applications relies on how accurately the decoding algorithms can classify brain activities ( Lotte et al., 2018; Krusienski et al., 2011; Bashashati et al., 2007; Lotte et al., 2007). Visual and auditory evoked potentials are signals due to external stimulation and define reactive BCI applications. Reactive BCIs are suitable for people who have severe disabilities, for example, individuals in locked-in states ( Chaudhary et al., 2022). Depending on the disability type and severity, the specific signal type is selected to meet individualized user needs. For example, a person with a visual impairment is not likely to use a visually evoked potential-based BCI but may find an auditory cue-based stimulation more useful. While reactive BCIs are time-synchronized to external stimuli, active ones offer intuitive use for functional autonomy. For visually evoked potential, studies found that not all individuals with disabilities perform equally for varied types of external visual stimulation, e.g., steady-state visual evoked potential vs. P300, and checkerboard flashing vs. row-column flashing-based BCIs ( Severens et al., 2014; Combaz et al., 2013; Townsend et al., 2010). Thus, a BCI system is unlikely to work well for all users, and poor performance with one system does not mean no system would work for that user. Finally, a passive BCI is applicable when monitoring users' affective states, such as detecting drowsiness. There is no one-size-fits-all; nonetheless, BCI-based ARTs require assessments of users' perspectives of gaining functional autonomy and codesign to best fulfil individualized needs ( French et al., 2024; Huggins et al., 2015; Nijboer et al., 2014; Huggins et al., 2011). Individualized BCI design can be guided by the ICF's emphasis on activity limitations and participation restrictions. For example, a user's ability to engage in daily tasks or social roles may be constrained by both impairments and contextual barriers, which BCI-based ARTs can help overcome.

The principles of rehabilitative BCIs rely on promoting neuroplasticity, which refers to the adaptive behavior of central and peripheral neural networks. Neuroplasticity is a critical ingredient of the motor learning process and is at the center of BCI-driven rehabilitation ( Flesher et al., 2021; Gao et al., 2022; Saha and Baumert, 2020; Dobkin, 2007). Neurostimulation modalities are the main components of neural encoding that externally modulate neural activities in target brain areas or peripheral neural networks to repair paretic functional abilities by rendering neuroplasticity ( Flesher et al., 2021; Mrachacz-Kersting et al., 2016). They are feedback elements of some closed loop or open loop rehabilitative BCIs, mostly restimulating impacted neural networks in the brain and central and peripheral nervous systems. Studies proposed BCI-based rehabilitation strategies featuring transcranial magnetic stimulation and direct/alternating current stimulation for repairing stroke lesions in the brain ( Keser et al., 2022; Van der Groen et al., 2022; Yang et al., 2021; Antal and Herrmann, 2016). Some implantable options include deep brain stimulation and intracortical microstimulation ( Allert et al., 2018; Willett et al., 2023, 2021; Oxley et al., 2016). While the primary applications of neurostimulation techniques are in neurorehabilitation, they can also be used to improve signal quality and users' ability to operate a BCI ( Won et al., 2023). ( Lorach et al. 2023) developed a rehabilitative BCI featuring epidural nerve stimulation for rectifying spinal cord injury. Other studies presented BCI-driven functional electrical stimulation for rehabilitating impaired upper or lower limb functions ( Brunner et al., 2024; Jovanovic et al., 2021; Mrachacz-Kersting et al., 2021, 2016). BCI-driven neurofeedback has been reported to regulate the cortical-subcortical networks and assists in modulating brain signals for cognitive or functional recovery ( Silversmith et al., 2021; Sitaram et al., 2017; Orsborn et al., 2014). Notably, both neurofeedback and neurostimulation may involve decoding users' intentions from brain activities to operate ARTs. Neurostimulation is an essential element of a bidirectional BCI, specifically for neural encoding; however, a more detailed investigation on neurostimulation is beyond the scope of this review, and the subsequent sections will emphasize neuroimaging modalities.

3 Advances in BCI technologies

3.1 Implantable vs. non-implantable neuroimaging techniques

Neuroimaging is a critical element of neural decoding, which defines the appropriateness of BCI-based ARTs across diverse user groups with different types of disabilities ( Patrick-Krueger et al., 2024; Edelman et al., 2024; Karikari and Koshechkin, 2023; Moghimi et al., 2013). Typically, no single modality offers the spatial, spectral and temporal resolution for mapping all required complex brain functions, resulting in limited BCI control signals ( Saha et al., 2021; Martini et al., 2020; Min et al., 2010). However, recently developed implantable speech BCIs have demonstrated up to 50-word decoding and constructing thousands of sentences by integrating language models ( Willett et al., 2023, 2021; Moses et al., 2021). Electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG) and microelectrode arrays (MEA) capture fine temporal features (i.e., electrical activities). However, the sensors are either sparsely distributed (spatially) or localized within an area of interest only. Functional magnetic resonance imaging (fMRI) records high-resolution spatial features of the brain, but the unmanageable size and slow hemodynamic signals are unsuitable for many real-time applications ( Sitaram et al., 2007). As an alternative, functional near-infrared spectroscopy (fNIRS) has lower spatial resolution but is more practical, mainly due to its portability and ease of use ( Naseer and Hong, 2015). On the other hand, electric and magnetic field-based neuroimaging techniques, i.e., EEG and MEG, offer better speed and bandwidth. MEG lacks portability and is barely usable outside a specialized setting ( Bu et al., 2023). Table 2 illustrates the key characteristics of the neuroimaging modalities and their applicability in BCI-based ARTs ( Saha et al., 2021; Hramov et al., 2021; Deffieux et al., 2018; Nicolas-Alonso and Gomez-Gil, 2012; Min et al., 2010).

Feature

EEG

MEG

ECoG

MEA

fMRI

fNIRS

Key characteristics of neuroimaging techniques and their applicability in BCI applications.

Saha et al. (2021), Hramov et al. (2021), Deffieux et al. (2018), Nicolas-Alonso and Gomez-Gil (2012), and Min et al. (2010).

BCI, brain-computer interface; EEG, electroencephalography; MEG, magnetoencephalography; ECoG, electrocorticogram; MEA, microelectrode array; fMRI, functional magnetic resonance imaging; fNIRS, functional near infrared spectroscopy.

From a translational perspective, EEG is still one of the most viable neuroimaging modalities due to its portability, easy maintenance and low cost. It does not involve any surgical procedure, like a craniotomy for implantable ECoG and MEA. Moreover, EEG could enable wearable BCIs using tiny recording setups like ear EEG ( Kaongoen et al., 2023). However, EEG signals are highly nonstationary and nonlinear due to time-variant and subject-specific anatomical, psychological, physiological, and environmental factors ( Saha et al., 2023, 2021; Saha and Baumert, 2020; Saha et al., 2019, 2017a, b). Furthermore, EEG signals do not offer the required spatial resolution for mapping localized brain functions because the signals attenuate through the outer brain layers, skull, scalp, skin and hairs ( Miinalainen et al., 2019; Cohen, 2017). This issue is somewhat alleviated with implantable ECoG and MEA, which record functionally relevant localized signals ( Vansteensel et al., 2016). However, the higher risk associated with craniotomy diminishes the utility of these neuroimaging modalities. For example, in cases where the long-term trajectory of user benefits and associated risk factors remains unclear to stakeholders. There are implanted alternatives that use less extensive surgery, such as Stentrode, a new type of neuroimaging introduced by Synchron TM ( Oxley et al., 2016). This system does not require craniotomy. Instead, it is computer-guided, with a minimally invasive procedure used to guide the Stentrode into a blood vessel within the brain. Studies have found that the signals are comparable to their counterparts with more extensive surgery, such as ECoG and MEA. ( Mitchell et al. 2023) discovered Stentrode to be a safe implantable endovascular BCI for people with severe paralysis. However, a limitation of Stentrode is that it records brain signals from only the major blood vessels; thus, a question remains if this technology is scalable to any brain area. Sub-scalp EEG has recently become another viable option for recording finer resolution signals than EEG and requires less maintenance than ECoG and MEA ( Duun-Henriksen et al., 2020). Generally, signal quality tends to improve with increasing extensive surgery in current neuroimaging modalities of BCI ( Figure 3). For example, the invasive Utah intracortical eletrode array and minimally invasive stentrode offer significantly superior signal quality as compared to EEG and is suitable for diverse BCI-based ARTs ( Mitchell et al., 2023; Grani et al., 2022; Oxley et al., 2016; Maynard et al., 1997).

3.1.1 Implantation lifespan and brain development

The appropriateness of a neuroimaging modality requires careful consideration, weighing up risks and benefits ( Rubin et al., 2023; Leuthardt et al., 2021; Sierra-Mercado et al., 2019), especially in cases of implantable BCI-based ARTs for people of different ages with diverse disabilities. Current implantable electrodes are increasingly biocompatible, provide good-quality signals for up to several years, and hold a promise to operate a BCI successfully. However, findings show that the neural signal quality deteriorates over time due to implant-related factors such as tissue encapsulation and material degradation ( Woeppel et al., 2021; Ferguson et al., 2019), and reimplanting sensors is a complex task due to surgical risk factors ( Sponheim et al., 2021; Viana et al., 2021; Woeppel et al., 2021; Sierra-Mercado et al., 2019). The necessity of reimplantation and post-operative maintenance are critical aspects of implantable BCIs over the lifespan of a user. An implantable BCI is often permanent, and the device is kept inside the brain due to anatomical changes in complex brain structure and risk factors associated with surgical extraction before potential reimplantation ( Fry et al., 2023). A question remains whether the current implantable BCI can offer long-term use while minimizing the chances of reimplantation. Further advances in neural sensor technologies, decoding algorithms and computing platforms in miniaturized format may lengthen the lifespan of implantable BCIs ( Shaeri et al., 2024; Steinmetz et al., 2021). However, more studies investigating the implant lifespan and safety considerations of implantation and reimplantation are essential to substantiate the usability of implantable BCI-based ARTs for people with disabilities.

In pediatric BCI, where the benefits of early implantation might take advantage of neurodevelopment and plasticity, the changing environment around implanted sensors over the lifespan is another essential aspect. Around 90%-95% of human brain growth occurs by the age of 5-7 years, although cognitive experiences evolve over the lifetime ( Zhou et al., 2024; Bethlehem et al., 2022; Hedman et al., 2012; Brown and Jernigan, 2012; Peters, 2006) ( Figure 4). Non-implantable neuroimaging, specifically EEG, offers BCI-based ARTs for pediatric users living with movement and speech disabilities ( Kelly et al., 2023; Jadavji et al., 2022). However, there is a lack of studies to substantiate the potential use of implantable BCI for pediatric users. The human brain undergoes anatomical transformations during its lifespan; for example, it expands and shrinks in the early and late ages ( Bethlehem et al., 2022). Moreover, electrode displacements post-implantation may contribute to increased chances of reimplantation ( Göransson et al., 2021; Morishita et al., 2017; Lumsden et al., 2015). As neural technologies advance and growing evidence are accessible for researchers and clinicians, the BCI-based ARTs may soon evolve in children with severe disabilities after careful technical and ethical considerations ( Bergeron et al., 2023). Although initially assumed inappropriate, cochlear implant is a notable example that now benefit pediatric users and demonstrate the promise of implantable neurotechnologies for people of all ages ( Chennareddy et al., 2025). Other examples of implantable neurotechnologies, though not mainstream, include responsive neurostimulation to reduce the dominance of epileptic seizures and deep brain stimulation to treat dystonia-related motor symptoms and disabilities ( Hartnett et al., 2022; Hale et al., 2020). In some cases, non-implantable BCIs tend to be more appropriate, for example, EEG-based BCIs for the elderly ( Belkacem et al., 2020).

3.2 Neural signal processing and pattern recognition

The most advanced BCIs utilize the brain's electrical activities recorded through neuroimaging techniques such as EEG, ECoG and MEA ( Edelman et al., 2024; Willett et al., 2023, 2021; Moses et al., 2021; Sponheim et al., 2021; Saha et al., 2021; Edelman et al., 2019; Oxley et al., 2016; Rao et al., 2014; LaFleur et al., 2013). Other state-of-the-art modalities include MEG to measure magnetic fields produced by the brain's electrical currents and fNIRS to record the brain's hemodynamic activities ( Bu et al., 2023; Naseer and Hong, 2015). These methods output time-domain signals corresponding to the user's intentions or cognitive states. Traditionally, various digital signal processing techniques filter out undesired noises or artefacts from raw neural signals to enhance the detectability of user's intentions or cognitive states ( Lotte et al., 2018; Krusienski et al., 2011; Bashashati et al., 2007; Lotte et al., 2007). Then, handcrafted feature extraction follows a classifier to evaluate the decoding accuracy of a BCI. However, with the recent developments in artificial intelligence-based algorithms, nonlinear activation functions replace handcrafted feature engineering for attributing more generic features associated with users' intentions or cognitive states ( Barmpas et al., 2023; Roy et al., 2020; Fahimi et al., 2019). Convolutional neural network (CNN)-based architectures are popular among the state-of-the-art artificial intelligence techniques. The raw neural signals may require preprocessing to enhance signal quality and transformation to represent the signals as compatible with CNN or other architectures ( Kamble et al., 2023a, b; Garćıa-Salinas et al., 2023). For example, raw neural signals are directly compatible as input to a 1-dimensional CNN or long short-term memory network architecture; however, the time-domain signals are incompatible as input to 2-dimensional CNN architectures. In this case, converting time-domain neural signals into 2-dimensional images through time-frequency analyses such as short-time Fourier transform and wavelet transform is typically performed.

Recent advancements in BCI technologies have significantly improved their performance. An implantable MEA has demonstrated an excellent 94% decoding accuracy for a 50-word classification task for a speech neuroprosthesis ( Willett et al., 2023). Non-surgical EEG systems, while less precise, show around 60% average accuracy for decoding up to 6 words ( Bhalerao and Pachori, 2025). A non-implantable EEG-based speller BCI can also provide high accuracy, such as 82% average accuracy, featuring visually evoked potential and few-shot learning ( Miao et al., 2024). However, decoding using the non-surgical EEG-based speller paradigm is much slower than word decoding by implantable BCIs. The trade-off between speed and accuracy is important while selecting an appropriate BCI type for a user, by considering the disability and user-centric technical specifications fulfilling individualized user needs and preferences. Although surgical options offer superior performance to non-surgical BCIs, surgical options are highly individualized and tailored to demonstrate user-specific high decoding accuracy. On the contrary, EEG signals are more accessible to many users with advanced decoding algorithms, some of which are generalizable across users.

The time-domain neural signals fluctuate over time and across users due to diversity in anatomical, physiological, psychological and cognitive characteristics ( Saha and Baumert, 2020). These inter-session and inter-subject variabilities cause differences in training and testing feature distributions, leading to covariate shifts. Covariate shift occurs when training and testing feature domains differ due to inherent variability in data characteristics, resulting in poor decoding accuracy ( Saha and Baumert, 2020; Azab et al., 2019, 2018; Jayaram et al., 2016). For covariate shift adaptation, a BCI typically requires tedious calibration using data from a new target subject or the same subject on a new session. Studies demonstrated significant inter-session and inter-subject variabilities, even in healthy user cohorts ( Saha et al., 2019, 2017b; Kang and Choi, 2014; Kang et al., 2009). The impact of these variabilities should be more prominent for users with disabilities due to the diversity in impairments post-neurological incidents ( Park et al., 2016).

Studies proposed various transfer learning strategies in BCI decoding algorithms for covariate shift adaptation ( Azab et al., 2019, 2018; Jayaram et al., 2016). Inter-session and inter-subject domain adaptation is an example of transfer learning in BCI. However, most transfer learning techniques require at least a few training data for decoding algorithm calibration while the goal is a fully zero-training BCI that promotes wide dissemination of this technology across diverse user groups. Inter-subject associative BCI is feasible requiring no training data from the target user when the training and testing subjects share similar brain dynamics ( Saha et al., 2023, 2019, 2017a, b). Quantifying inter-subject associativity is a way to predict the performance of fully zero-training BCI. From a translational perspective, generalized decoding algorithms can disseminate BCI-based ARTs to a large cohort by minimizing the calibration requirements ( Roy et al., 2020; Saha et al., 2017b). Even so, a large cohort of people (15%-30%) encounter BCI deficiency, which refers to BCI system's inability to interpret users' brain signals ( Park and Jun, 2024; Bamdadian et al., 2015; Vidaurre and Blankertz, 2010).

4 Translational socioeconomic outlook of BCI technologies

The ICF framework provides a valuable foundation for policy and regulatory development. Its standardized language for describing functioning and disability can facilitate cross-sector communication among clinicians, engineers, policymakers, and users, supporting ethical and inclusive deployment of BCI technologies. BCI-based ARTs can assist people with disabilities in accessing mainstream socioeconomic life, where they can contribute to the ethical and economic dynamics of society. The ICF framework supports inclusive policy development by highlighting contextual factors affecting participation across all health conditions. Regardless of scientific breakthroughs, users' perspectives on using BCI-based ARTs and their safety are critical ( Patrick-Krueger et al., 2024; Soldado-Magraner et al., 2024; Bergeron et al., 2023; Saha et al., 2021; Naufel and Klein, 2020; Sample et al., 2019; Klein and Ojemann, 2016; Nijboer et al., 2013). Individualization of BCI-based ARTs by user-centric design strategy is vital where users can participate in policy-making and take informed decisions on their preferences, comforts and interaction with the technologies ( Shrivastava et al., 2025; Branco et al., 2025). While all stakeholders must ensure the ethical use of BCI, users with disabilities and their caregivers should be at the center of research, development, and policies for maximizing the benefits of advanced BCIs. Informed consent from the users or their caregivers is the precursor to using a BCI after adequately making the user aware of the risk factors and the expected benefits. Some users with disabilities may experience cognitive or communication difficulties; thus, ensuring they are fully informed about risks and benefits may be challenging ( Willett et al., 2023, 2021; Moses et al., 2021; Vansteensel et al., 2016). Consent may be challenging in children ( Bergeron et al., 2023); and careful consideration is required as their perspective of using a BCI may change by the time they reach adulthood. They may question their parent's or guardian's decision on whether to implant a BCI in their early years. Studies underlined the importance of engaging BCI stakeholders considering ethics policies to ensure transparent communication with users before obtaining consent ( Patrick-Krueger et al., 2024; Soldado-Magraner et al., 2024; Bergeron et al., 2023; Saha et al., 2021; Naufel and Klein, 2020; Sample et al., 2019).

Standardizing BCI-based ARTs and their lawful utilization is crucial for positive societal change ( Patrick-Krueger et al., 2024; Soldado-Magraner et al., 2024; Bergeron et al., 2023; Chandler et al., 2022). BCI is an emerging field, and authorities should act promptly to set up regulations that include all stakeholders' opinions due to the fast evolution of BCI-based ARTs in diverse areas. The Brain/Neural Computer Interaction Horizon 2020 project defined six primary objectives of BCI: (1) restore (e.g., unlocking the residual ability of completely locked-in); (2) replace (e.g., BCI-based ARTs); (3) enhance (e.g., improved user experience in video games); (4) supplement (e.g., interactive virtual/augmented/mixed reality glasses); (5) improve (e.g., upper/lower limb rehabilitation post stroke); and (6) research tools (e.g., decoding brain activity with real-time neurofeedback) ( Brunner et al., 2015). The development of regulatory guidelines may address the socioeconomic concerns and reflect the defined objectives of BCIs in these application areas. ( Patrick-Krueger et al. 2024) discussed the clinical perspectives of implantable BCIs and the importance of regulatory approvals after clinical trials of the state-of-the-art technologies. A question remains whether BCI guidelines could limit their use after carefully evaluating the risks vs. benefits and socioeconomic impact trajectories ( Patrick-Krueger et al., 2024; Soldado-Magraner et al., 2024; Coin and Dubljević, 2023; Sample et al., 2019).

Emerging cybersecurity measures are also essential translational elements of BCI-based ARTs. Confidentiality, integrity and availability are critical cybersecurity components to ensure ethical BCI use ( Liv and Greenbaum, 2023; Bernal et al., 2023). Confidentiality prevents unauthorized access to sensitive neural data, integrity ensures data precision, and availability maintains the infrastructure to provide secured data access to authorized stakeholders ( Goodman and Rowland, 2021). Illicit access to signature neural activities mapped to passwords or visual and auditory stimulus and their manipulation can damage an individual's social presence ( Armengol-Urpi et al., 2023; Ienca and Haselager, 2016). Wireless BCI systems may be hackable, so adequate cybersecurity measures are essential ( Bernal et al., 2023; Ajrawi et al., 2021; Ienca and Haselager, 2016). This becomes concerning if a BCI system has the potential to alter functional neural circuits, posing risks to core aspects of a user's identity such as personality, memory, and emotional regulation ( Steinert and Friedrich, 2020; Ienca and Haselager, 2016). Altering human cognitive capacity is challenging because it is unclear when mental changes are reversible ( Dunlap et al., 2020; Schmitz-Luhn et al., 2012). Inappropriate use of neural data can distort intended BCI outcomes, potentially leading to unethical or unsafe control of ARTs. Integrated cryptographic encryption and access control into BCI may offer a secure option, fulfilling BCI cybersecurity requirements. Figure 5 illustrates the translational components of BCIs, from technology standardization to ethics policies, algorithm development and essential cybersecurity measures that promote BCIs as promising ARTs for people with disabilities, contributing to their widespread dissemination. The translational elements of BCI can be described from the perspectives of technology, ethics, and cyberspaces. The first includes advanced neural sensors, generalized digital signal processing and pattern recognition algorithms, and individualized ARTs. The elements of ethical space include surgical risk factors associated with electrode implantation, technology standardization, and lawful utilization. Finally, key cybersecurity measures are data confidentiality, availability, and integrity.

5 Conclusion

Recent advancements in neuroimaging and artificial intelligence are rapidly transforming BCI technologies, expanding their potential as ARTs for people with disabilities. BCIs offer novel pathways for communication and control, particularly when conventional methods are ineffective or unavailable.

To ensure broader accessibility and adoption, the development of generalizable digital signal processing and pattern recognition algorithms remains a critical priority. At the same time, the success of BCI-based ARTs increasingly depends on personalized, user-centered design approaches that reflect the diverse and evolving needs of individuals with disabilities.

Given the sensitive nature of neural data and its potential to reveal deeply personal information, the ethical, legal, and social implications of BCI use must be addressed proactively. Establishing robust regulatory frameworks and inclusive guidelines-developed in collaboration with all stakeholders-is essential to ensure the responsible and equitable deployment of BCI technologies.

Ultimately, the future of BCI-based ARTs lies in balancing technological innovation with ethical foresight, personalization with scalability, and accessibility with security. This integrated approach will be key to realizing the full potential of BCIs in enhancing autonomy, communication, and quality of life for people with disabilities.

In summary, researchers and developers must adopt an inclusive and personalized design strategy for BCI-based assistive and rehabilitative applications, engaging all stakeholders from the early stages of the development process. Such a user-centric methodology could maximize the benefit to people with disabilities while maintaining ethical and moral standards for sustainable BCI use for people with disabilities who need this technology most. Future research should consider the opinions of all stakeholders, which will help justify and guide technological advancements in individualized BCI-based ARTs.

Statements

Author contributions

SS: Writing - review & editing, Writing - original draft, Conceptualization. PK: Writing - review & editing, Supervision, Writing - original draft. CA: Supervision, Writing - review & editing, Writing - original draft. OK: Writing - original draft, Writing - review & editing, Supervision. AM: Writing - original draft, Supervision, Writing - review & editing.

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The author(s) declared that financial support was not received for this work and/or its publication.

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Keywords

assistive and rehabilitative technologies, brain-computer interface, generalized algorithms, neuroimaging techniques, user-centric design

Citation

Saha S, Karlsson P, Anderson C, Kavehei O and McEwan A (2026) Individualized brain-computer interface for people with disabilities: a review. Front. Hum. Neurosci. 20:1738876. doi: 10.3389/fnhum.2026.1738876

Received

03 November 2025

Revised

10 January 2026

Accepted

21 January 2026

Published

10 February 2026

Volume

20 - 2026

Edited by

Noman Naseer, Air University, Pakistan

Reviewed by

Shugeng Chen, Fudan University, China

Thiago Bulhões, Federal University of São Paulo, Brazil

Updates

Copyright

*Correspondence: Simanto Saha, simanto.saha@sydney.edu.au

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Frontiers | Individualized brain-computer interface for people with disabilities: a review

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Brain-computer interfaces (BCIs) facilitate functional interaction between the brain and external devices, enabling users to bypass their typical peripheral ...

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This article looks at how Napkin AI can be used in the classroom and it's intended uses.

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What Napkin AI actually is

Where I use it: brainstorming a unit

It works because the visual canvas matches how I actually think. Linear note-taking forces me to commit to an order before I know what the order is. Napkin lets me put everything on the table and let the structure emerge.

Where I use it: creating an infographic for class

Where I use it: a meeting tool

Where I do not use it

Napkin vs the alternatives

Mind mapping tools (MindManager, XMind): more rigid hierarchical structure, no AI extraction, no infographic output. Use those for strict hierarchies, Napkin for fluid relationships.

Design tools (Canva, Piktochart): better for polished final infographics, worse for the thinking-while-drafting phase. Use those for finals, Napkin for thinking.

Where it fits in my classroom workflow

Napkin lives in my pre-lesson prep stack - the place where I am generating ideas and structuring units. Once I have the structure, I move it into my actual classroom platform. Specifically the unit map flows into KiwiBee's curriculum planner , the worksheets and printables I draft from Napkin's content go into , and the daily classroom delivery happens through the AI worksheet generator . Napkin is the thinking tool. The teaching happens elsewhere. KiwiBee's whiteboard tool

Where this fits in KiwiBee

If you want AI visuals that flow into actual lessons and homework, KiwiBee adds an AI lesson and resource creator , , and a free worksheet generator with images . Try it free at a whiteboard tool for live lessons . app.kiwibee.io

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Napkin AI: Note-Taking, Brainstorming, and Infographics | KiwiBee Blog

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Review of Napkin AI for educators. Discover how this AI tool transforms notes into visual diagrams, brainstorming canvases, and infographics.

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This pamphlet gives lesson plans for lessons you can teach using AI.

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This article looks at examples of how AI has been used in the classroom and lessons they have learned from using it.

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This Work has been submitted to Bulletin of the American Meteorological Society. Copyright in