[Eeglablist] ICA on lowpass / highpass filtered data

[Krebber, Martin]

Hi,

thanks a lot for your input. I tried Simons PCA approach on a couple of datasets and it seems like I need less than half of my components to explain 99% of variance in my high high frequency data.

I am wondering, though, if this approach is appropriate since PCA and ICA work differently. As far as I understood, PCA tries to maximize the variance explained of each component, whereas ICA tries to maximize the independence of the components. So if, for instance, 64 PCs explain 99% of my variance, 64 ICs might explain much less.

Another thing that seems worth mentioning is that when I run the same procedure over the low pass data or the original unsplit data I need even fewer PCs to explain the same percentage of variance. So this procedure does not really explain why my ICA on the high pass filtered data takes so much longer than the ICAs in low frequency or unsplit data.

Could it be that it's a question of independence rather than of variance explained? Is there a way to estimate how many independent sources there are in the data?

Thanks!

Martin



On 17.05.2013 00:25, 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><mailto: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><mailto: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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