[Eeglablist] ICA on lowpass / highpass filtered data

[Arnaud Delorme]

Dear Martin and Simon,

I would be careful about pre-processing using PCA. I was just reviewing a PhD thesis (http://infosci.otago.ac.nz/carl-leichter-phd) that showed using simulations and real data that removing PCA components creates artifacts in the data, especially in the power spectrum. The same problem would arise if you run ICA and preprocess using PCA. I would be especially careful if you plan on removing these components from the original data, and then analyze the EEG power spectrum.

Arno

On 16 May 2013, at 15:25, Simon-Shlomo Poil wrote:

> Dear Martin,
> 
> As Makoto says, you make the channels less independent of each other.
> It might be resonable* to reduce using PCA. One way to determine the
> number of relevant dimesions could be
> [COEFF, SCORE, LATENT] = princomp(EEG.data');
> tmp  = cumsum(LATENT);
> nr=find(tmp/tmp(end)>0.975,1);
> 
> , which gives you the number of principle components explaining 97.5 %
> of the variance.
> 
> *you can find previous mails discussing pro-/con-  of PCA reduction on
> the this list (I remember there was even a paper in prep? I didn't see
> it come out)
> 
> Best wishes
> Simon
> 
> --
> Simon-Shlomo Poil, Dr.
> 
> 2013/5/16 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:
>> Dear Martin,
>> 
>> If you apply a band-pass filter, your channel data become less independent
>> of each other i.e. rank-reduced.
>> 
>> Imagine you apply an extreme band-pass filter, say 10-11Hz. All of your
>> channel data look very much like each other.
>> 
>> Makoto
>> 
>> 
>> 2013/5/16 Krebber, Martin <martin.krebber at charite.de>
>>> 
>>> Hi all,
>>> 
>>> 
>>> I am currently working on an analysis were I split the data into low and
>>> high frequency portions using a lowpass (cutoff 35 Hz) and a highpass
>>> (20 Hz) filter, respectively. The idea behind this approach is to do the
>>> ICA artefact rejection seperately on low and high frequency data in
>>> order to be better able to reject high frequency muscle artefacts and
>>> obtain a clearer brain signal in the gamma range.
>>> 
>>> My problem is that, especially with the highpass filtered data, ICA
>>> takes a very long time (roughly 5-10 times the usual) and even then the
>>> decomposition does not look very clean. I tried to reduce the
>>> dimensionality of the data (from 128 to 96) by applying the PCA
>>> parameter in pop_runica and it is way faster. Is it justified, or maybe
>>> even recommended to reduce the data dimensionality after filtering out a
>>> considerable portion of the signal? And if so, is there a rule of thumb
>>> about how much to reduce the data dimensionality?
>>> 
>>> Thanks for any suggestions!
>>> 
>>> Regards,
>>> Martin
>>> 
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>> 
>> 
>> 
>> 
>> --
>> Makoto Miyakoshi
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego
> 
> 
> 
> --
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