It’s more a methods and mathematical question rather than a pure software question.
I have one participant with about 500x 2 second epochs of EEG data with 64channels in two difference sessions, let’s call them PRE and POST. I also have a 1 minute rest measurement for each session
I want to perform inverse connectivity measurements in the alpha range. For that I use the baseline measurement as noise covariance matrix (and use /fsaverage/bem/fsaverage-ico-5-src.fif)
My issue is that if I use spectral_connectivity_epochs with wpli method I only get 1 value for PRE and 1 value for POST. I could not find any mean to assess the significance of each and every comparison in the dimension 2 connectivity matrix.
Therefore i used spectral_connectivity_time on each epoch, providing me a dimension 3 connectivity matrix. By running a z-test (left skewed distribution), and p-value correction for multiple comparisons I was able to check for significance between surface areas.
I would like to ask whether splitting trials in 2 second epochs, then individually assess connectivity is a correct method to go or whether there are underlying issues with the method used.