[Eeglablist] Data Processing Steps for ICA?

[Arnaud Delorme]

There is also this page just in case. 

http://sccn.ucsd.edu/wiki/Quick_Rejection_Tutorial

Makoto, I have slightly edited your new page.

> > 2. When and how to perform re-referencing:
> It seems I should re-reference to average before ICA, but there seems to be some counterarguments, too:
> http://sccn.ucsd.edu/pipermail/eeglablist/2014/008309.html
> http://sccn.ucsd.edu/pipermail/eeglablist/2014/008854.html
> http://sccn.ucsd.edu/pipermail/eeglablist/2003/000090.html
> 
> Just re-reference to the average reference. Don't forget to reject one channel after this.

Yes, I would reference to average reference before running ICA as well although this is not critical (you can always rereference after running ICA and this will transform the ICA matrix as well). After running ICA for years, it seems that decomposition using the average reference are slightly better. However, there is no formal comparison I know of. In theory, since it is all linear, it will not change anything for ICA (although because of numerical implementation it might).

> > I'm not pretty sure whether to include EOG channels either, as there seems to be pros and cons: 
> http://sccn.ucsd.edu/pipermail/eeglablist/2014/008854.html
> http://sccn.ucsd.edu/pipermail/eeglablist/2007/001801.html
> 
> Do include EOG channels for ICA. I made a wrong answer last time.

I would include the EOG channels if you recorded them with the same reference as ICA. Otherwise do not include them.

Hope this helps,

Arno


> 
> Makoto
> 
> On Sat, Oct 25, 2014 at 2:54 AM, Paul Yu-Chun Chang <Y.Chang at lipp.lmu.de> wrote:
> Dear All,
> 
> I have 32-channel Neuroscan recordings (ca. 1 hr per subject) and am recently trying to use ICA to remove blinks and eye movements from the data (EEGLAB version 12.0.2.4b). After checking out some relevant discussions/wiki pages/tutorials I find myself however still a bit confused (particularly regarding re-referencing, baseline correction, and channels to be included when running ICA) and am having some further questions. I'd really appreciate your help.
> 
> Some essential steps as I understand now involve (pls correct me if I'm wrong):
> 
> 1. Reject bad epochs/channels to prune the data.
> (source: http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA ; http://sccn.ucsd.edu/eeglab/workshop06/handout/Practicum_3_ICA_Process.pdf)
> 2. Train ICA firstly on 1 Hz high-pass filtered pruned dataset.
> 3. Apply ICA weights to the same subject's 0.1 Hz high-pass filtered data and evaluate components.
> (source: http://sccn.ucsd.edu/pipermail/eeglablist/2011/004424.html)
> 4. Interpolate bad channels.
> 
> I'm however not so clear about:
> 
> 1. When to perform base-line correction: 
> It seems most people suggest not to do it before ICA training based on Groppe's 'split-half' paper: 
> http://sccn.ucsd.edu/pipermail/eeglablist/2010/003080.html
> http://sccn.ucsd.edu/pipermail/eeglablist/2014/008854.html
> But there are some counterarguments:
> http://sccn.ucsd.edu/pipermail/eeglablist/2012/005513.html
> So I guess it's still better to do it after ICA?
> 
> 2. When and how to perform re-referencing:
> It seems I should re-reference to average before ICA, but there seems to be some counterarguments, too:
> http://sccn.ucsd.edu/pipermail/eeglablist/2014/008309.html
> http://sccn.ucsd.edu/pipermail/eeglablist/2014/008854.html
> http://sccn.ucsd.edu/pipermail/eeglablist/2003/000090.html
> 
> I'm not pretty sure whether to include EOG channels either, as there seems to be pros and cons: 
> http://sccn.ucsd.edu/pipermail/eeglablist/2014/008854.html
> http://sccn.ucsd.edu/pipermail/eeglablist/2007/001801.html
> 
> Also is it all right to use average of M1/M2 (mastoid sites) for re-referencing instead? Should M1/M2 be included for ICA training?
> 
> Many thanks!
> 
> Paul
> 
> 
> 
> 
> 
> 
> 
> 
> -- 
> 
> Paul Yu-Chun Chang
> Graduate School Language & Literature Munich - Class of Language
> Ludwig-Maximilians-Universität München
> Schellingstraße 10
> 80799 München, Deutschland
> Email: Y.Chang at lipp.lmu.de
> 
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> 
> 
> -- 
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
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