[Eeglablist] Data analysis with few channels
azuberer at googlemail.com
Wed Jun 3 12:04:03 PDT 2015
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
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
> To unsubscribe, send an empty email to
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