[Eeglablist] ICA pipeline questions

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Mon Sep 11 11:55:02 PDT 2017


Hello Samran, here's notes for you below, good luck on your eeg adventures!


******NOTES FOR SAMRAN********

*you need to understand/know exactly what PREP is doing, and if you want
comments on that, you should list the steps you think PREP is doing.

*your pipeline 1 seems okay. One can drop channels instead of PCA.

*Note It's generally not recommended to interpolate channels before ICA.
*Not sure why you are running PREP again (I guess it's okay if it does
exactly the same thing as earlier.
*Reject epochs at step 13 after reviewing the data, unless for some reason
you trust that A) you have removed all artifactual ICs and B) there are no
remaining artifactual periods in the epoched data

*In your Pipeline 2, it's up to users whether or not they run a second ICA
after pruning the data of Bad ICs. I would not recommend that, but you can
look in past eeglablist answers, and in Makoto's processing suggestions,
and in publications using ICA for EEG.

*Double check that you don't need to move the resampling to the be first or
second step.


Your question #1
***ICA in eeglab does not care if there are discontinuities in the data, so
it does not matter if you give it continuous data with breaks, or epoched
data. It mixes up the time points and focuses on spatial patterns (not
temporal patterns).

your question #2
I've specified above that the Data Analyst (you) needs to be sure there is
no dirty data going into your averages and metrics. That is regardless of
whether or not you already pruned your data by rejecting ICs. Be careful to
NOT DO everything automatically until you have checked the results of your
pipelines (at the epoch level and averaging level), and your are sure
really sure that you don't need to go extra cleaning after bad IC rejection.
In short, there may still be dirt in the data after rejecting artifactual
ICs. You need to personally check whether there is or is not remaining dirt
in the data, and you need to be careful not to "assume" that things are
working, but rather "check fully" that things are working.

Your question #3b
The PCA correction is correct. Personally I've had better success with
"dropping a channel before ICA to account fix average referencing's drop in
rank" instead of the PCA flag in runica. In other words, after average
referecing, and before ICA, I drop 1 channel rather than use the PCA
reduction.

Your Question $3b
Don't interpolate before ICA.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20170911/ab62f90b/attachment-0001.html>


More information about the eeglablist mailing list