Hello Brainstorm,
I have a question about how the "average selected signals" option works in the permutation absolute mean test.
Suppose I have 13 cortical auditory evoked potentials (CAEPs) in group A and 21 CAEPs in group B.
I would like to compare the activity of a cluster of channels within each CAEP between the two groups. I am guessing there are two approaches to specifying this cluster of channels. Either I specify the relevant channel names in the sensor types or names, OR I can first make a cluster and extract the relevant channel data using extract cluster time series.
My first question is properly understanding what "average selected signals" means.
Suppose I have the below CAEP in panel A with the relevant channels selected. If I extract these channels I would get panel B and if I then "average selected signals" will I get panel C? (in this case this was made using the "mean" cluster function which averages across the channels).
If panel C is indeed how "average selected signals" work, I have the second question. Let's say that instead of making a cluster and extracting cluster time series I specified the channel names like the below screenshot. If I select "average selected signals" will it first average these specified signals within each CAEP and compare that between the two groups?
I ask these questions because I wasn't sure if by selecting "average selected signals" Brainstorm would average across channels meaning that for channels with opposite polarities they would cancel each other out.