[Mne_analysis] ICA Analysis

[Bianca Islas, BS]

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Once again, thank you MNE team!

The suggestions you gave us, helped out immensely.  Mr. Clemens Brunner, your blog on ICA and regression analysis were real eye-openers!
Following your lead on creating a copy, and making some tweaks here and there to fit our needs, it would appear that ICA000, as we were hoping/expecting is the component we will be continually removing.  This certainly streamlines the process of running our script on all of our CNT files.

Once again thanks your for your time, suggestions, and resources!

Best,
Bianca Islas
UNLV PEPLab Research Assistant
From: Brunner, Clemens (clemens.brunner at uni-graz.at)
Sent: Tuesday, August 13, 2019 12:15 PM
To: Discussion and support forum for the users of MNE Software
Subject: Re: [Mne_analysis] ICA Analysis

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Hi!

I haven’t looked at your results in detail, but maybe my blog posts about removing ocular artifacts via ICA (https://cbrnr.github.io/2018/01/29/removing-eog-ica/) or via regression (https://cbrnr.github.io/2017/10/20/removing-eog-regression/) might be helpful.

Clemens


From: mne_analysis-bounces at nmr.mgh.harvard.edu <mne_analysis-bounces at nmr.mgh.harvard.edu> On Behalf Of Bianca Islas, BS
Sent: Tuesday, August 13, 2019 20:24
To: mne_analysis at nmr.mgh.harvard.edu <mne_analysis at nmr.mgh.harvard.edu>
Subject: [Mne_analysis] ICA Analysis

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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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