I am trying to find the best strategy to analyze a set of EEG time-frequency data from 24 subjects, using 64 electrodes. This is an exploratory study as I have no a-priori hypothesis on where or when changes in two frequency bands might be observed. In this case I have found that using non-parametrical statistical testing might be the best soulution: in particular, cluster-based permutation testing is widely used in cognitive electrophysiology.

However this approach is not feasible as I have a 2X2X2 within subject design, and it is not possible to look at interaction with permutation testing. On the other hand, running an ANOVA on the whole data-set might well result in an high number of false positives.

A solution to my proble might be to define ROI(s), so to perform ANOVA(s) on a limited number of electrodes within a time window. However I have no clue on how to define such ROI(s).

What is the best strategy according to you?


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