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

Thu Sep 21 01:26:31 PDT 2017

Hi Roberto!

Regarding your second question, I've found that Extended Infomax ICA is much faster in MNE-Python than in EEGLAB (even much faster than binica). So instead of days or weeks, computation time could go down to hours.

You can do many EEGLAB analyses with MNE-Python - I'd encourage you to read the tutorials and examples to get an overview. There's also a mailing list quite similar to this one if you have questions.

Clemens

> On 20 Sep 2017, at 20:27, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
> 
> 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 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?
> 	• When creating STUDY, outside-brain ICs can be kicked out.
> 	• 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.
> 	• 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 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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