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<p style="margin: 0 0 1em 0; color: black;">Do you have to do
within-subjects classification, or could you cross-validate on the
subjects? (I guess that is option 4.) That would also simplify the
permutation testing.</p>
<p style="margin: 0 0 1em 0; color: black;">If you need to stick to
cross-validation within people, I'd prefer splitting the dataset into two
halves (first half of the run, second half of the run, which I assume is
your option 1) over option 2: adding more cross-validation folds just
compounds the potential confounds, and there's nothing special about 10
folds.</p>
<p style="margin: 0 0 1em 0; color: black;">Jo</p>
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<p
style="color: black; font-size: 10pt; font-family: Arial, sans-serif; margin: 10pt 0;">On
May 30, 2016 4:28:19 AM Richard Dinga <dinga92@gmail.com> wrote:</p>
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8"><div
dir="ltr"><div><div><div><div><div>Hi,<br></div>I am running within subject
searchlight on fast eventrelated data collected only in one run per
subject. Therefore no naturally independent blocks to do my crossvalidation
on. So far I thought about 3 approaches:<br><br>1. delete 20 seconds of
data between training and test set<br>2. don't do anything and perform 10
fold CV without any adjustments <br></div></div>3. use some non CV based
statistic like correlation distance <br><br></div><div>although 2 and 3
doesn’t seems reasonable.<br></div><div><br></div>Also, I would like to do
a permutation test to find out if my results are significant, but I am not
sure how to set up a permutation scheme since it seems like everything is
dependent and nothing is exchangeable. <br><br></div><div>Any suggestions
what to do in this situation? <br></div></div>
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