Hi Justin,
Question 1 : Is it acceptable to interpolate the source results for each gait cycle to the average latency between events and then proceed to average? Clearly this cannot be done with the raw data or the frequency content of the signals would be distorted.
If the goal is only to produce an ERP, with no time-frequency or connectivity analysis, reinterpolating the EEG recordings in time may help you observe effects with slow dynamics across trials and subjects. Another option is to reinterpolate time-frequency decompositions of the single trials, this could be better to observe faster components.
Question 2: If so, is it possible to somehow detach the source results from the original data? If I timewarp each segment, the source results will not be the same time length as the data used to estimate them.
I've just edited the process "Standardize > Uniform epoch time" to support correctly source results. It now detaches the interpolated source files from the recordings, by setting the field DataFile to .
5 Import cleaned eyes-open rest data and compute data covariance. I am using the identity for noise covariance since I cannot accurately estimate sensor noise. I am happy to hear thoughts on this.
7 Estimate sources - I am playing with MN constrained and LCMV unconstrained based on some recommendations in the tutorials. I am also happy to hear thoughts on this.