[Eeglablist] ICA / ICLabel Pipeline Questions

[Clement Lee]

Hi Tom,

A few thoughts on your points:
1) Using a hard threshold with IC Label as you suggested is more objective
than the commonly accepted procedure of manually determining and
discarding non-brain components, so that seems okay.
2) Have you installed AMICA for EEGLAB? Not sure if RELICA has this
dependency but that might be it. See
https://sccn.ucsd.edu/~jason/amica_web.html for AMICA installation
instructions.
3) Filtering EMG before ICA may actually be advantageous since EMG may be
better modeled as non-stationary sources (as opposed to stationary sources,
which ICA separates). Maybe someone with more preprocessing expertise can
comment here.
4) Re-referencing after running ICA only changes the channel data but not
the component data. If you are using ICA as an artifact rejection tool (and
analyzing the channel data instead of the component data) then
re-referencing as you've described - to optimize ICA first, then arrive at
your most familiar montage for analysis - makes sense.

Best,
Clement Lee
Applications Programmer
Swartz Center for Computational Neuroscience
Institute for Neural Computation, UC San Diego
858-822-7535


On Wed, Mar 4, 2020 at 11:26 PM Tom Bullock <thomas.bullock at psych.ucsb.edu>
wrote:

> 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
>
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