[Eeglablist] Data analysis with few channels
mmiyakoshi at ucsd.edu
Wed Jun 3 13:07:48 PDT 2015
Well, if you want to use the regression based method but can't find the
existing solution, then you have to write code. If you have only 3 channels
I don't think ASR in clean_rawdata() work properly either.
Maybe you can use temporal approach... try ARfitStudio. You need to mark
every single artifact onset though.
On Wed, Jun 3, 2015 at 12:04 PM, Agnieszka Zuberer <azuberer at googlemail.com>
> Dear Makoto,
> regarding the small number of channels and the small number of available
> data points I would have suggested a regression based artifact removal
> instead of an ICA. I am using only 3 electrodes and had very bad results
> with an ICA. However, I am still looking for a proper regression based
> artifact removal procedure in EEGlab or MATLAB. I have checked the extended
> EEGlab plugin list but could not find a regression based artifact removal
> Please advice.
> 2015-06-03 20:36 GMT+02:00 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:
>> Dear Emmanuelle,
>> Here is Makoto's preprocess pipeline
>> If you re-reference before ICA, don't forget to reduce the data rank by
>> 1. You can either reject any one channel or use 'pca' option in runica to
>> specify the rank which EEG.nbchan-1.
>> Don't forget to clean your data before ICA. Try artifact subspace
>> reconstruction (ASR) in clean_rawdata(). You can even choose the parameters
>> so that it keeps the original data length.
>> Also for ICA make sure that you have sufficiently large number of
>> datapoints, which channel^2 x 30 or larger when 32ch.
>> For high-pass, use 1Hz and transition 0.25-0.75Hz. This is very high
>> compared with other recommendations.
>> On Mon, Jun 1, 2015 at 1:20 PM, Emmanuelle Renauld <
>> emmanuelle.renauld.1 at ulaval.ca> wrote:
>>> Hi all,
>>> I have been thinking a lot about the best way to analyse my data,
>>> considering that I have very few channels (8. Thus 7 after re-referencing).
>>> So far, I do:
>>> 1. Re-referencing.
>>> 2. High-pass
>>> 3. Low-pass
>>> 4. ICA decomposition
>>> However: with only 7 channels, I often had very bad result at ICA.
>>> Looking at the signals, I saw many bugs happening on nearly all electrodes
>>> at the same time. If I cut the signal around that bug (ex, remove some data
>>> from EEG.data and EEG.times), the spectrum is usually already cleaner, and
>>> the ICA decomposition also works better (for instance, alpha frequencies
>>> are then well separated from the eye blinks and eye movements components).
>>> So I started thinking about "epoching" my data, but I don't have events
>>> to do that. Something like cutting my data into windows of maybe 1 second,
>>> removing windows where the signal is too big, and then computing spectrums
>>> and ICA. I did it manually, cutting usually 2-3 sections in the data, the
>>> worst parts, of usually around 10 seconds each. I was supervising the
>>> results, and it worked, but if I start doing it automatically, I fear that
>>> the number of windows rejected increase. What do you think would be the
>>> effect of computing ICA or spectrums on such annexed data? Could it, for
>>> instance, create false frequencies?
>>> I have two types of data: at rest, or doing a DDT task.
>>> Thank you very much!
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>> Makoto Miyakoshi
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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> Agnieszka Zuberer
> Möhrlistr. 92
> 8006 Zürich
> Tel.: +41 76 29 51 321
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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