[Mne_analysis] Applying ICA with No Components Selected out but Signal Changes Using Default Parameters

[Rockhill, Alexander P.]

Also, of note the ica scale is off by quite a lot in the plot_sources plot, it is way too zoomed in.

Alex

Translational NeuroEngineering Laboratory
Division of Neurotherapeutics, Department of Psychiatry
Massachusetts General Hospital, Martinos Center
149 13th St Charlestown #2301, Boston, MA 02129
________________________________
From: mne_analysis-bounces at nmr.mgh.harvard.edu <mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of Rockhill, Alexander P. <AROCKHILL at mgh.harvard.edu>
Sent: Tuesday, July 2, 2019 3:25 PM
To: mne_analysis at nmr.mgh.harvard.edu
Subject: [Mne_analysis] Applying ICA with No Components Selected out but Signal Changes Using Default Parameters

Hi,

    In an analysis, I am running:

ica = ICA(method='fastica', n_components=n_components,  # n_components=None
          random_state=seed)
ica.fit(inst2)
...
inst2 = ica.apply(inst2, exclude=ica.exclude)

    and when I skip all intermediate steps and just fit the ICA and apply it with an empty list for ica.exclude the signal still changes, quite a bit. I thought if no components were selected out and all the max PCA components were used the signal would be unchanged or basically unchanged. Is this a bug or something with my implementation?

Thanks,

Alex

Translational NeuroEngineering Laboratory
Division of Neurotherapeutics, Department of Psychiatry
Massachusetts General Hospital, Martinos Center
149 13th St Charlestown #2301, Boston, MA 02129
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