It is important to realize at the outset this webpage is not intended to be an introductory tutorial on how to analyze ERP data. Indeed, that particular topic is far too broad to be covered in a single webpage - or many webpages for that matter. If you are reading this and have no ERP experience, then I strongly recommend you read Steve Luck's book - An Introduction to the Event Related Potential Technique - before proceeding any further. Better, yet - a graduate level course or experience in an ERP lab would be preferable. We want to stress here that it is quite easy to record ERP data with the MUSE - however, interpretation of said data does require some expertise.

Further, we assume that the reader of this webpage is familiar with MATLAB. Again, a one page MATLAB tutorial is not possible.

Further, we assume that the reader of this webpage is familiar with MATLAB. Again, a one page MATLAB tutorial is not possible.

## Data Format

If you are using our code to record data directly from MUSE, then the saved ERP data is recorded as a cell array with 2 cells, each with a 4 x n matrix where 4 is the number of MUSE channels (TP9, AF7, AF8, TP10) and n is the number of recorded time points. Note, with our code and research preset AD this will be a multiple of 500 as we record 1 second of data (500 samples) for each trial. The cells reflect each of the two experimental conditions. On this website, the main task we present is a visual oddball paradigm. For the sample data that is attached below, the first cell reflects the data for the oddball trials and the second cell reflects the data for the control trials. Note, the trial data is concatenated during recording. As such, one needs to reshape the matrix to get data that is channel x time x trial (e.g., epoched or segmented data).

If you load the recorded MUSE data into MATLAB you will see a variable EEG with two cells as outlined above. For the purposes of the pre-processing examples below, we will just work with the data for the oddball condition.

In MATLAB:

If you load the recorded MUSE data into MATLAB you will see a variable EEG with two cells as outlined above. For the purposes of the pre-processing examples below, we will just work with the data for the oddball condition.

In MATLAB:

**MUSEDATA = EEG{1};**## Data Processing

We use a series of steps to pre-process the data for ERP analysis. Full code is not provided here - simply examples of each step.

## 1. Demean

MUSE data is recorded with a large DC offset (also, it is not in uV units but "MUSE" units). As such, the first step needed to process the MUSE data is to remove the mean (typically about 800 MUSE units).

In MATLAB:

This step removes the mean of the data for channel 1 (TP9) from the data. You should see that the mean before the demean is in the 800's and after is close to zero. Note, you would have to repeat this step for the other three channels. Also note, for the purposes of this tutorial we will only work with the first channel as well. Of course, you would need to tweak your code to see repeat the analysis for each channel.

In MATLAB:

**mean(MUSEDATA(1,:)) % display the mean of the first channel prior to demeaning****MUSEDATA(1,:) = MUSEDATA(1,:) - mean(MUSEDATA(1,:)); % remove the mean from the first channel****mean(MUSEDATA(1,:)) % display the mean after the the original mean has been removed**This step removes the mean of the data for channel 1 (TP9) from the data. You should see that the mean before the demean is in the 800's and after is close to zero. Note, you would have to repeat this step for the other three channels. Also note, for the purposes of this tutorial we will only work with the first channel as well. Of course, you would need to tweak your code to see repeat the analysis for each channel.

## 2. Filter

The next step in pre-processing is to filter the data. We will filter the data using a 2nd order bandpass Butterworth filter. Again, this solution is not ideal as this is not "continuous" data, but it does work and does not leave large artifacts from our experience.

In MATLAB:a

In MATLAB:a

**plot(MUSEDATA(1,1:1000)); % plot a segment of the MUSE data prior to the filter****[b,a] = butter(2,[0.5 15]/(500/2)); % construct the filter****MUSEDATA(1,:) = filtfilt(b,a,MUSEDATA(1,:)); % apply the filter**

hold on; % ensure the original plot stays on screen

plot(MUSEDATA(1,1:1000)); % overlay the filtered datahold on; % ensure the original plot stays on screen

plot(MUSEDATA(1,1:1000)); % overlay the filtered data

## 3. Segmentation

In the next analysis step we reshape our "continuous" data into segmented (epoched) data. In a typical ERP experiment this would be done with event markers, here however, we know that we have exactly 500 samples of data for each trial. So, we will just use this information to reshape the data. Also note, we do not have a baseline prior to the onset of the event of interest, as such, we our segments at 500 Hz go from 0 ms to 1000 ms.

In MATLAB:

In MATLAB:

**size(MUSEDATA) % show that the data matrix is currently 4 x n in size**

number_of_trials = size(MUSEDATA,2)/500; % determine the number of trials represented in the datanumber_of_trials = size(MUSEDATA,2)/500; % determine the number of trials represented in the data

**MUSEDATA = reshape(MUSEDATA,4,500,number_of_trials); % use reshape to create segments**

size(MUSEDATA) % show the new size of the data matrixsize(MUSEDATA) % show the new size of the data matrix

## 4. Trim Data

We will state at the outset that this step is not necessary - but a general rule of thumb in ERP research is that the longer each segment is the more artifacts you will detect and the more trials will be "lost". As such, we found through observation of the data for this experiment that we only needed the first 300 data points. Thus, to reduce the number of artifacts that we encountered we "trimmed" the last 200 data points from the data matrix.

In MATLAB:

In MATLAB:

**size(MUSEDATA) % show that the data matrix is currently 4 x n in size****MUSEDATA(:,301:end,:) = []; % remove the last 200 data points**

size(MUSEDATA) % show the new size of the data matrixsize(MUSEDATA) % show the new size of the data matrix

## 5. Baseline Correction

Typically in ERP studies a baseline correction is used to "zero" the waveforms for each segment. To do this, researchers use a baseline from 200 ms prior to stimulus onset to the stimulus onset. The mean is computed for the baseline, and the mean is then subtracted from each time point of data. This is done on a segment by segment basis.

With the MUSE, we do not have baseline data from prior to stimulus onset, so instead we use the first 50 ms of data as the baseline. The MATLAB code below shows how the baseline would be computed and subtracted from a single trial of data. Note, this code would have to be put in a loop to repeat this step for each segment for each channel.

In MATLAB:

With the MUSE, we do not have baseline data from prior to stimulus onset, so instead we use the first 50 ms of data as the baseline. The MATLAB code below shows how the baseline would be computed and subtracted from a single trial of data. Note, this code would have to be put in a loop to repeat this step for each segment for each channel.

In MATLAB:

**baseline = mean(MUSEDATA(1,1:25,1));**

MUSEDATA(1,:,1) = MUSEDATA(1,:,1) - baseline;MUSEDATA(1,:,1) = MUSEDATA(1,:,1) - baseline;

## 6. Artifact Detection

In ERP studies one wants to compute averages that do not contain artifacts - blinks, saccades, or other non ERP noise. There are many different approaches to doing this, the one we use here is simple. We compute the variance of each segment of data - we then discard any segments in which the variance of the segment exceeds 200 uV^2/ms. Alternatively, one could use a max-min approach in which segments are discarded exceed some absolute difference between the maximum and minimum values of the segment - for instance 100 uV. Note, this code would have to be put in a loop to repeat this step for each segment for each channel.

In MATLAB:

In a later step you could then remove any trials with NaN values. There are other ways to do this of course.

In MATLAB:

**check_value = var(MUSEDATA(1,:,1));**

if check_value > 200

MUSEDATA(1,:,1) = NaN;

endif check_value > 200

MUSEDATA(1,:,1) = NaN;

end

In a later step you could then remove any trials with NaN values. There are other ways to do this of course.

## 7. Averaging

The ERP waveform is the average of all the segments for a condition. As such, once artifacts are removed one simply needs to generate the mean across the segments to get the ERP waveforms for each channel and condition. It is worth noting here, than in our laboratory we typically collapse across the two frontal channels (AF7,AF8) and the two ear channels (TP9, TP10) to create a pooled or average channel (AF, TP).

In MATLAB:

In MATLAB:

**ERPDATA = mean(MUSEDATA,3);**## 8. Quantifying ERP Components

Typically, one wants to quantify ERP components for statistical analysis. For example, with the oddball data here one might want to get the score for the N200 and P300 for each participant. This process is quite involved, but a simple algorithm for doing so is:

1. Create average ERP waveforms for each subject for the oddball and control conditions.

2. Create an average difference waveform for each subject by subtracting the average control waveform from the average oddball waveform.

3. Create grand average ERP waveforms by averaging ERP conditional waveforms (oddball, control) across all subjects.

4. Create grand average difference ERP waveforms by averaging the difference waveforms across all subjects.

5. Plot the grand average conditional and difference waveforms.

6. Establish the time point at which the N200 and P300 are maximal on the grand average difference waveforms.

7. Take the mean using a window of +/- 25 ms centered on these two times of the average difference waveforms for each subject - these are your "scores" for the N200 and P300. The logic here is simple - these scores should statistically differ from zero if the N200 and P300 components exist - i.e., there is a statistical difference in the subject waveforms meaning the effect is real. You could then compare these scores between groups (young versus old, happy versus sad) or within groups (time 1 versus time 2).

1. Create average ERP waveforms for each subject for the oddball and control conditions.

2. Create an average difference waveform for each subject by subtracting the average control waveform from the average oddball waveform.

3. Create grand average ERP waveforms by averaging ERP conditional waveforms (oddball, control) across all subjects.

4. Create grand average difference ERP waveforms by averaging the difference waveforms across all subjects.

5. Plot the grand average conditional and difference waveforms.

6. Establish the time point at which the N200 and P300 are maximal on the grand average difference waveforms.

7. Take the mean using a window of +/- 25 ms centered on these two times of the average difference waveforms for each subject - these are your "scores" for the N200 and P300. The logic here is simple - these scores should statistically differ from zero if the N200 and P300 components exist - i.e., there is a statistical difference in the subject waveforms meaning the effect is real. You could then compare these scores between groups (young versus old, happy versus sad) or within groups (time 1 versus time 2).

## Other Options

The code in the ZIP files HERE brings .muse data into EEGLAB. The code also saves the EEG structure that is created in Brain Vision Analyzer format. In this manner, one can use either EEGLAB/ERPLAB or Analyzer to preprocess the EEG data.

Note, this works for both continuous data that is generated via MUSELAB or from the sample code we have provided here. Because this data is assumed to reflects trials (the oddball task, the learning task) the above MATLAB scripts add markers to the data for segmentation purposes. Markers are added at the start of each trial (i.e., 0ms) and by condition - a "1" for whatever condition one is and a "2" for the other condition. This code would need to be adapted for other experiments wherein there are more than two conditions.

Note, this works for both continuous data that is generated via MUSELAB or from the sample code we have provided here. Because this data is assumed to reflects trials (the oddball task, the learning task) the above MATLAB scripts add markers to the data for segmentation purposes. Markers are added at the start of each trial (i.e., 0ms) and by condition - a "1" for whatever condition one is and a "2" for the other condition. This code would need to be adapted for other experiments wherein there are more than two conditions.