# data[].attributes.body
May ChatGPT help you to construct the transcript for the English presentation?

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Can you find the effect of Chabychev I and II filters?

# data[].attributes.subject
Can you find the effect of Chabychev I and II filters?

# data[].attributes.body
(A) Raw lead II ECG trace. (B) Lead II ECG trace After High pass Chabychev I filtering. (C) ECG trace (Lead II) before High pass filtration. (D) ECG trace after application of the High pass Chebyshev II filter.

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1. Please sit with your group members for 12/11.

2. List the major features of the assigned parameters or filters on Padlet.

3. Check whether you can find the effect on the biopotential signal by the assigned figures. Please describe the difference between the various filtered signals on Padlet.

4. Please use ChatGPT to generate the COMPREHENSIBLE transcript for presentation.

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Can you find the effect of different orders?

# data[].attributes.subject
Can you find the effect of different orders?

# data[].attributes.body
The surface electromyogram of the unﬁltered MEP is shown as a continuous red line from

the same subject as before. The signal was ﬁltered with a 5 Hz Butterworth HPF with different orders,

a ﬁlter with 1st-order, with 2nd-order, with 4th-order and with 8th-order was chosen for comparison.

The time window in this graph was 100 ms in duration. The 1st-order ﬁlter shows the smallest

difference compared to the raw signal. The lower graph shows the areas of deviation of the different

ﬁltered signals to the unﬁltered MEP.

# data[].attributes.headline
Can you find the effect of high-pass and low-pass filters?

# data[].attributes.subject
Can you find the effect of high-pass and low-pass filters?

# data[].attributes.body
(A) Raw lead II ECG trace. (B) Lead II ECG trace After Low pass Chabychev I filtering. (C) ECG trace (Lead II) before High pass filtration. (D) ECG trace after application of the High pass Chebyshev I filter.

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1. Check your group for 12/11 and the assigned parameters of the filters or the different filters.

2. Search online how the assigned parameter may change the performance of the filter. Please focus on how it would affect the output signal.

# data[].attributes.headline
Can you find the effect of high-pass and low-pass filters?

# data[].attributes.subject
Can you find the effect of high-pass and low-pass filters?

# data[].attributes.body
(A) Raw lead II ECG trace. (B) Lead II ECG trace After Low pass Chabychev I filtering. (C) ECG trace (Lead II) before High pass filtration. (D) ECG trace after application of the High pass Chebyshev I filter.

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111263039

# data[].attributes.body
111263018蔡聿絜

# data[].attributes.headline
Can you find the effect of cut-off frequence?

# data[].attributes.subject
Can you find the effect of cut-off frequence?

# data[].attributes.body
The single unﬁltered MEP raw signal from a representative participant is shown as acontinuous bold red line. The other signals (on the upper graph) were ﬁltered with a 1st-order

Butterworth HPF with different cut-off frequencies (1 Hz, 20 Hz, 40 Hz, and 80 Hz). The time window

in this graph was 100 ms in duration. The graph shows the MEP signal 1 ms after TMS trigger. The

lower graph shows the areas of deviation of the different ﬁltered signals to the unﬁltered MEP.

# data[].attributes.body
The effect of various filter designs for biopotential recording.

1. Please find the features of the specific filter/parameters of the filter for biopotential signals.

2. Please describe how the signal changes after filtering.

3. Please describe the differences between different filters/parameters.

# data[].attributes.headline
Can you find the effect of cut-off frequence?

# data[].attributes.subject
Can you find the effect of cut-off frequence?

# data[].attributes.body
The single unﬁltered MEP raw signal from a representative participant is shown as acontinuous bold red line. The other signals (on the upper graph) were ﬁltered with a 1st-order

Butterworth HPF with different cut-off frequencies (1 Hz, 20 Hz, 40 Hz, and 80 Hz). The time window

in this graph was 100 ms in duration. The graph shows the MEP signal 1 ms after TMS trigger. The

lower graph shows the areas of deviation of the different ﬁltered signals to the unﬁltered MEP.

# data[].attributes.headline
Can you find the effect of different orders?

# data[].attributes.subject
Can you find the effect of different orders?

# data[].attributes.body
The surface electromyogram of the unﬁltered MEP is shown as a continuous red line from

the same subject as before. The signal was ﬁltered with a 5 Hz Butterworth HPF with different orders,

a ﬁlter with 1st-order, with 2nd-order, with 4th-order and with 8th-order was chosen for comparison.

The time window in this graph was 100 ms in duration. The 1st-order ﬁlter shows the smallest

difference compared to the raw signal. The lower graph shows the areas of deviation of the different

ﬁltered signals to the unﬁltered MEP.

# data[].attributes.headline
Can you find the effect of Chabychev I and II filters?

# data[].attributes.subject
Can you find the effect of Chabychev I and II filters?

# data[].attributes.body
(A) Raw lead II ECG trace. (B) Lead II ECG trace After High pass Chabychev I filtering. (C) ECG trace (Lead II) before High pass filtration. (D) ECG trace after application of the High pass Chebyshev II filter.