I am performing source-estimation on walking EEG data. The data is epoched to each subjects gait cycle before being imported to brainstorm. The epochs vary in size between subjects, but are generally between 0.8-1.5 seconds. I was wondering what parameters I should choose for calculating noise covariance. We record data at 500 Hz.
Maybe try using some resting recordings (with no walking)?
All the recommendations I could give you are summarized in the introduction tutorials:
Ok, we have resting state data for each subject that was recorded the same day before walking. So from what I'm understanding from the tutorials, I should process the resting state data the same way as I am the walking data(filter,asr,remove and interpolate the same bad channels) but just don't epoch it. Then I should import this resting state data for each subject alongside the walking data, but calculate the noise covariance matrix using only a section(about 40 seconds) of the resting state data. This noise covariance matrix would then be applied to the walking data while calculating the sources. Does this sound correct?
interpolate the same bad channels
If the goal is source estimation: do not interpolate bad channels.
You may interpolate them for computing sensor-level group statistics, but mark them as bad for source estimation.
Then I should import this resting state data for each subject alongside the walking data
No need to "import": right-click on the "Link to raw file" > Data covariance > Compute from recordings.
calculate the noise covariance matrix using only a section(about 40 seconds) of the resting state
Compute the noise covariance from longer recordings if you can (up to 10min)
This noise covariance matrix would then be applied to the walking data while calculating the sources.
Right-click on the noise covariance computed for the resting recordings > Copy to other folders.