[Mne_analysis] ICA Analysis

[Bianca Islas, BS]

        External Email - Use Caution        

MNE Team,

First and foremost we’d like to thank you for the responses and suggestions to our last email.  As we get closer to finalizing our Py script, you have all certainly given us some food for thought.

We now have a new concern relating to the ICA analysis.  We have been consulting the documentation and have followed the tutorial for ICA analysis: 
https://mne.tools/stable/auto_tutorials/preprocessing/plot_artifacts_correction_ica.html

However, our component scores seem to be all over the place.  It’s also saying that there are three EOG channels in the latent sources plot, when there should only be HEO and VEO. Historically, we have used the Semlitsch algorithm [Semlitsch HV, Anderer P, Schuster P, Presslich O., (1986) A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP, Psychophysiology,23(6):695-703] with no issues.  Is there a way to use this method in MNE-Python instead?  We realize that the Semlitsch algorithm may be outdated, is there a reason/reference ICA may stand apart from this algorithm?

As always thank you for your assistance,

UNLV PEPLab
Bianca Islas
Research Assistant


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