I think PyPREP and ICALabel should catch most large artifacts, and smaller artifacts should normally not matter in such a large dataset (if one missed artifact changes the outcome, something’s wrong with the analysis anyway). If you really want to make sure you are not missing anything, I don’t think there’s an alternative to manually reviewing the data – at least that’s what I am still doing, and having automated artifact annotations as a starting point makes that process reasonably fast. If you want more automated tools, you could take a look at autoreject — autoreject 0.5.1 documentation, but I doubt it will add anything substantial if you’re already using PyPREP/ICALabel, so I wouldn’t really recommend it.
Adding back the original Pz reference channel before PyPREP: should I apply average reference first?
cbrnr
(Clemens Brunner)
6
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