[Eeglablist] a few questions about your ICA-based preprocessing pipeline

[Makoto Miyakoshi]

Dear Roberto,

> 1) My initial concern is that I want to run regression analyses through
MNE-python on epoched and artifact-free data (this is because the matlab
toolbox for it is quite buggy). Is that at all compatible with your ASR
pipeline?

I have no experience in MNE-python thing, I don't even know what it
represent for!
So I can't say anything for sure. Sorry.

> My plan was to run ICA on all data throught eeglab, and then exports the
clean datasets through MNE. Do you think it is feasible?

Technically yes, as long as the thing can import Matlab variables in one
way or another.

> 2) Running ICA on 256 components for 30 subjects may last days (if not
weeks), so I was wondering what your opinion on that is. Since the goal is
just to find blinks and other noise, I was thinking that I could ask it to
estimate a smaller number of components - but then what would the
consequences be?

If you are only interested in decomposing (for removing) eye blinks AND if
you don't want to spend a week, use 'pca' option from infomax runica() GUI.
You see 'extended', 1 there. Add 'pca', 35 to decompose 35 ICs only. This
should be enough to capture eye blinks. Change the number as necessary. It
should work even with 10-20 if your target is eye blinks.

> 3) I am using your batch code for multiple subjects, and I am a little unclear
about the last steps of the ASR pipeline
<https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline#Alternatively.2C_cleaning_continuous_data_using_ASR_.2812.2F23.2F2016_updated.29>
that
are not present in the batchcode (i.e., steps 13-15). If I understand well,
you suggest to create a STUDY just for clustered IC rejection, so that I
can select clustered ICs to apply on the whole STUDY. But then why do you
suggest to epoch the ICs and then create a full STUDY?

What I suggest in doing so is to select IC clusters at the group level
instead of opening 30 subjects and going through 256x30 ICs manually. My
suggested method uses power spectrum density (PSD) only for clustering,
with scalp maps as additional info to help evaluations. This should be done
with continuous data. As a result, it will create another set of .set
files. Does it make sense?

> On a related note - when and how exactly do I remove the IC from the
actual data?

   1. When creating STUDY, outside-brain ICs can be kicked out.
   2. When creating STUDY, ICs with dipoles with residual variance (i.e.
   difference from ideal dipolar projection on scalp) > 15 %, as a default
   value, can be rejected.
   3. When projecting the selected IC clusters, the ICs contained by the
   unselected clusters are rejected.

Makoto




On Sun, Sep 17, 2017 at 8:51 AM, Roberto Petrosino <
roberto.petrosino at uconn.edu> wrote:

> Dear Dr. Mayakoshi,
>
> I am a PhD Student in Linguistics at UConn, and I am currently working on
> a project testing the EEG response to words with a EGI 256-channel net. I
> am trying to use your ASR pipeline, and I have a few questions. I am sorry
> that I am writing directly to you, but I got no reply from the eeglab
> mailing list, so I was wondering if you could help out. Apologies in
> advance for my naive questions - this is my first time using ICA for
> artifact rejection.
>
> 1) My initial concern is that I want to run regression analyses through
> MNE-python on epoched and artifact-free data (this is because the matlab
> toolbox for it is quite buggy). Is that at all compatible with your ASR
> pipeline?
> My plan was to run ICA on all data throught eeglab, and then exports the
> clean datasets through MNE. Do you think it is feasible?
>
> 2) Running ICA on 256 components for 30 subjects may last days (if not
> weeks), so I was wondering what your opinion on that is. Since the goal
> is just to find blinks and other noise, I was thinking that I could ask it
> to estimate a smaller number of components - but then what would the
> consequences be?
>
> 3) I am using your batch code for multiple subjects, and I am a little unclear
> about the last steps of the ASR pipeline
> <https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline#Alternatively.2C_cleaning_continuous_data_using_ASR_.2812.2F23.2F2016_updated.29> that
> are not present in the batchcode (i.e., steps 13-15). If I understand well,
> you suggest to create a STUDY just for clustered IC rejection, so that I
> can select clustered ICs to apply on the whole STUDY. But then why do you
> suggest to epoch the ICs and then create a full STUDY? On a related note -
> when and how exactly do I remove the IC from the actual data?
>
> Many thanks for your help in advance.
>
> Best regards,
>
> -Roberto
>
> ----------
> Roberto Petrosino
> Ph.D. Student in Linguistics
> CT Institute for the Brain and Cognitive Sciences
> University of Connecticut
>
>
>


-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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