[Eeglablist] Data Processing Steps for ICA?

Fri Nov 21 01:09:55 PST 2014

Dear Makoto,

Many thanks for your reply. At one time I did see ghost signals (even
scarier than those on the wiki page) when I didn't remove a channel after
re-referencing.  Indeed this is profound to me, but thank you again for
your suggestions.

Best regards,

Paul

2014-11-16 23:21 GMT+01:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:

> > 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
>

-- 

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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