[Eeglablist] Data Processing Steps for ICA?

[Makoto Miyakoshi]

> If I just rereference after ICA, would ICA-matrix transformation also
take care of the data rank issue or should I still discard 1 channel?

Sometimes it does, sometimes not. Therefore I would say it does not. When
we got the weird results shown in that wiki page I directed, even Scott did
not know what was going on. In order to avoid this kind of confusion I
recommend you reject a channel. You can alternatively use pca option to
reduce the rank, but it is less straightforward in my opinion. I believe we
should simplify the data as much as possible, so remove any potential
source of later confusion.

Makoto

On Wed, Nov 5, 2014 at 12:29 AM, Paul Yu-Chun Chang <Y.Chang at lipp.lmu.de>
wrote:

> Dear Arno & Makoto,
>
> Many thanks for your reply and the suggested pages; they are indeed very
> helpful. One small question I still have, then, is that it is suggested in
> Makoto's page to discard (any) 1 channel from the data at the step of
> average-rereferencing (due to reduction of data rank by 1), before running
> ICA. If I just rereference after ICA, would ICA-matrix transformation also
> take care of the data rank issue or should I still discard 1 channel?
>
> Thank you very much,
>
> Paul
>
> 2014-11-05 9:03 GMT+01:00 Arnaud Delorme <arno at ucsd.edu>:
>
>> 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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>>
>
>
> --
>
> 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
>



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