Dear mne python users,
I am trying to take out eog artifacts using mne python artifact
correction with ICA. I do ICA on raw data that has both EEG and MEG
data. I first pick MEG ch., do ICA on them (ica.fit where 'picks' has
meg only) and find components to reject. Then I want to apply ICA
solution to the MEG part of data and move onto EEG.
I am doing it like so:
raw_meg_iced=ica.apply(raw, exclude=some_components) #some_components =
picks_eeg=mne.pick_types(raw_meg_iced.info, meg=False, eeg=True,
eog=False, stim=False, exclude='bads')
ica.fit(raw_meg_iced, picks=picks_eeg) # and so on ...
I was wondering if I am doing the right thing by doing ica.apply on the
whole raw object? Presumably, since I identify components from meg data
first, I want to apply ica to meg data only? But ica.apply doesn't have
'picks' function. The two functions that perform signal reconstruction
(_apply_raw and _pick_sources) seem to reconstruct the data from the pca
components derived from what was ica-ed - i.e the meg data in the first
instance. Yet the resulting raw_meg_icaed object still has original EEG
data and I can pick and ICA it subsequently. I can't figure out how it
knows to keep it in. Clearly I am missing something here.
Thanks a lot for your help,