[Eeglablist] a few questions about your ICA-based preprocessing pipeline

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Wed Sep 20 13:04:42 PDT 2017

Hi Roberto a few extra thoughts here below. Good luck with that
high-density EEG data!!

See the following excellent website for learning about ICA classification.
I recommend students to do at least 500 classifications, it's doable on
smartphone. The tutorials and examples are excellent resources.

See the multiple ICA classification plugins (MARA, ADJUST, SASICA, IC-MARC)
for some near-automatic methods for removing unhealthy ICs. The articles on
each of those plugins are also useful if you're just beginning with
dense-EEG and ICA.

Overall, I recommend running ICA without PCA, but then just focusing on the
first 35 ICs. Usually one can just pick out the top 10 neural ICs and
reject the rest. It depends on why you're doing ICA (just to find blinks)
or to actually examine the ICs. eeglab is built to help you stay in ICA
land, rather than go back to channel-level space. This of course depends on
multiple factors, such as whether you have good similar neural ICs for most
of your subjects.

MNE python should have no problem eating the data that results from eeglab.
If you find the exact method, please share it with the eeglab list. If you
develop a method for passing data between eeglab and mne-python that would
also be a cool thing to share.

Overall, a good bet is to lean on published articles using dense-EEG and
ICA for best methods/approaches.

By the way which buggy matlab regression toolbox were you referring to ?
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