[Eeglablist] ICA / ICLabel Pipeline Questions

[Tom Bullock]

Dear EEGLAB users,

I’ve been working with an EEG dataset recorded from participants during a series of cold-pressor tests that is (quite understandably) heavily contaminated by noise (EMG, EOG, movement etc.).  I’m following Makoto’s preprocessing pipeline (thank you Makoto - this is super helpful!) and then using AMICA to decompose the signal and ICLabel to classify components prior to analysis.  I’m relatively new to ICA so I have several questions about the processing pipeline.  Any help or advice would be much appreciated!

1) I’m trying to figure out the best way to use the information from the IC Label classifier to accept/reject components.  There was a discussion on this mailing list that touched on this topic a couple of months back and one suggestion was to use RELICA in addition to ICLabel to determine the reliability of the components.  However I’m still not sure what rule to use for the IC Label classifier data?  For this specific dataset I think it is justified to use a fairly liberal rejection criterion (i.e. only keep components that I’m pretty confident are “brain” and get rid of anything else).  Would it be acceptable to use a hard threshold e.g. keep anything above p>.8 brain and remove anything else, or can you suggest a better way to do this? 

2) I would like to use the AMICA algorithm in RELICA, and the instructions suggest this is possible (https://github.com/sccn/relica <https://github.com/sccn/relica>) but when I open RELICA in the EEGLAB gui it doesn’t give me AMICA as an option.  Am I missing something here?  I have the most recent versions of EEGLAB and RELICA installed.

3) My typical approach to dealing with EMG when ICA isn’t part of my preprocessing pipeline is to low-pass filter the data at 30 Hz.  If I’m using ICA to isolate brain components, would it make any sense to low-pass filter at any stage of the processing pipeline, either before or after ICA?  Low-pass filtering prior to ICA would remove the bulk of the EMG, but I’m not sure if this would be a good thing for the quality of the decompositions.

4) I typically use an average mastoid reference for EEG preprocessing, but I understand that re-referencing to the average of the scalp channels is recommended prior to ICA.  If I did want to continue using the average mastoid reference would I just need to accept that my decompositions would be suboptimal, or is there  a better way to do this e.g. first re-reference to the average of the scalp channels, run ICA, then re-reference a second time to the average mastoids?  

Thank you in advance!

Tom Bullock, PhD

Project Scientist,
Department of Psychological and Brain Sciences,
University of California, Santa Barbara, USA
https://www.researchgate.net/profile/Tom_Bullock2
www.linkedin.com/in/tomwbullock