[Eeglablist] ICA pipeline questions

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Mon Sep 11 12:22:17 PDT 2017

Some followup points here for your Samran:

1. many researchers use and trust near fully automatic or fully automatic
pipelines. See for example TAPEEG. It really depends on their biases (e.g.,
being programmers/engineers versus EEG researchers) and their needs (e.g.,
having dozens versus thousands of files). Further, automated pipeleines can
be tweaked to your satisfaction, and can also provide "quality metrics" to
know when there are issues in the pipeline.

2. Your "Drop artifactual ICs" step can be complex. Some researchers only
keep neural ICs, some researchers only remove artifactual ICs, some
researchers fully trust the results of ICA classifcation plugins such as
Adjust, MARA, IC-MARA, and SASICA. If you haven't had a chance to use/test
the results form those plugins, please be sure to do so.

3. If you haven't had a chance to yet, make sure to avail yourself of the
excellent IC classification site from Luca at the following link. I usually
recommend to students and beginners to do at least 500 classifications

On Mon, Sep 11, 2017 at 11:55 AM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com
> wrote:

> 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/20170911/14d381d4/attachment-0001.html>

More information about the eeglablist mailing list