[Eeglablist] ICA on lowpass / highpass filtered data

[Simon-Shlomo Poil]

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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