[Eeglablist] Trouble rejecting ICs via STUDY

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Thu Nov 7 15:00:57 PST 2019

HI Dan, brief notes below, may you have cohesive IC dipole clouds!

This might be a bug, or have something to do with recent eeglab update, as
this step (from GUI, or through scripting) usually works well.
I'm pretty sure that doing the RV thresholding once a study has been built
does NOT affect the original files (they are not pruned of high RV ICs).
I think you are pruning your ICs in STUDY,
but then it is incorrect to assume that the IC pruned dataset has been
saved to the original single-subject .set file.
That may be why you are not seeing the IC dropping when re-opening a
single-subject data file.

Yes generally, this should prune down your total IC number considerably,
leaving primarily neural or almost-all-neural ones.
One can of course review/prune all IC RV info directly in each
single-subject file (outside of study, in a loop) to confirm that things
are okay - and then load them into study after select ICs have been removed
from the files at the single-subject file level.
I would suggest making and saving the study; and then opening the study
info and selecting the RV selection, and seeing if it works that way.
Another way do it would be at single subject level (make a loop that checks
RV for each IC dipole, makes list of ICs to drop, and then drops specific
ICs for that file).
If you haven't had a chance to, the IClabel plug, and other ICA classifiers
work well. For example SASICA also uses dipole info.

On Thu, Nov 7, 2019 at 9:45 AM Dillon, Daniel G. <ddillon at mclean.harvard.edu>

> Dear Colleagues:
> I’m working my way through Makoto’s incredibly helpful preprocessing
> pipeline and am confused about using the STUDY tool for IC rejection (step
> 13 in Makoto’s 5/1/2019 update). I’m using eeglab2019_0, and here’s what’s
> happening:
>   1.  I choose “File > Create study > Browse for datasets”;
>   2.  In the “Create a new STUDY set” GUI, I then select my three datasets
> (just testing this out on a few subjects to get it working) and make sure
> the “Update dataset info—datasets sorted on disk will be overwritten” box
> is checked;
>   3.  In the GUI I click “Select by r.v.”, and in the next GUI I click OK
> to reject dipoles (a) with > 15% r.v. and (b) that are outside the brain;
>   4.  In MATLAB, this text appears: “Selecting dipoles with less than
> %15.0 residual variance and removing dipoles outside brain volume in
> dataset”;
>   5.  I click OK in the “Create a new STUDY set” GUI and then “Saving
> dataset . . .” appears 3x (once per subject) in the MATLAB Command Window.
> I then see “Rebuilding designs . . .” and “Done.”
> This all seems correct, but if I then load any of my subjects’ datasets
> and choose “Tools > Locate dipoles using DIPFIT > Plot component dipoles”,
> I have the same number of dipoles as I did before creating the STUDY. In
> other words, it doesn’t look like any dipoles have been removed, which is
> what I thought was happening in steps 4 and 5 above. Then I thought that
> perhaps the dipoles aren’t actually removed but are instead just marked for
> removal, but I’m not seeing anything consistent with that when I look at
> EEG.dipfit.model or EEG.reject.gcompreject for individual subjects.
> Am I missing something here? I was expecting to end up with ~10-20 clean
> ICs per subject, and instead I still have the ~90 ICs that I started with.
> Not sure if it matters, but I am doing all this with continuous data.
> Consistent with MM’s pipeline, my plan was to complete this step and then
> epoch the IC-rejected data before creating a final STUDY.design for the
> group-level analysis.
> I’d be grateful for anyone’s insights—using the STUDY tool to reject ICs
> in this way seems like a particularly efficient way to go (if I can get it
> working).
> Thanks!
> Dan Dillon
> McLean Hospital
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