[Eeglablist] Trouble rejecting ICs via STUDY

Dillon, Daniel G. ddillon at mclean.harvard.edu
Thu Nov 7 06:18:05 PST 2019

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).


Dan Dillon
McLean Hospital

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