Independent channels stats question

[Matthew Davidson]

Hi, this is Matthew Davidson. I recently took the Fieldtrip EEG/MEG
Toolkit (Hi Robert and Jan-Mathis!), and have been diving into using
Fieldtrip more directly.

My question pertains to cluster-based correction when channels are
independent. My data is primarily intracranial EEG, and due to the
1/f^2 power drop-off, electrodes directly on the brain reflect local
activity much more strongly than sensors further away. As a result, we
treat them as independent. Now, I can force the Fieldtrip clustering
algorithm to not cluster across channels by setting:

cfg.neighbours = [];
cfg.minnbchan = 0;

but it still computes the maximum cluster size for a particular
permutation based on *all* the data. This seems... less sensitive
somehow, as if large clusters in one channel negatively impact the
significance of clusters in another channel.

Is there a better way to do this and still solve the MCP? E.g.,
compute the maxsum on each channel separately, and then use something
like FDR or Bonferroni correction on the maxsums across channels?

Thanks for any advice you may have, and thanks for producing fieldtrip!
Matthew

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