[Eeglablist] ICA on lowpass / highpass filtered data

Mon May 20 13:33:58 PDT 2013

Hi Martin,

My take: I think a large part of the reason for the disappointing
high-frequency decomposition is due to the extreme attenuation of the skull
of high-frequency low-amplitude sources. The independent components usually
extracted in ICA of EEG are primarily low frequency, plus muscle, eye, and
line noise. One might think that a low frequency, say midline theta,
component might contain gamma, possibly amplitude correlated to a certain
phase of theta. Personally I haven't had a lot of success with this in EEG.
So there is a question of how much brain signal is left in the high-pass
part. There is also a question of how intermittent the high-frequency
signal, or how non-stationary relative to other intermittent high-frequency
signals.

As for the computation time increase, I have noticed this kind of thing as
well, and don't know the reason, but have thought it had something to do
with caching data in the CPU-the next number multiplied in the low-pass data
might require fewer register or bit modifications.

I think your comments on PCA vs. ICA are right. ICA should extract
independent components regardless of variance. I don't think there is a way
to estimate the number of ICs without actually performing ICA. You can then
check pairwise mutual information of the sources and count the number of ICs
having less than some threshold of mutual information with all other
components, or the number of independent subspaces (with only pairwise
mutual info within themselves.)

Best,

Jason

From: eeglablist-bounces at sccn.ucsd.edu
[mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Krebber, Martin
Sent: Friday, May 17, 2013 9:28 AM
To: Simon-Shlomo Poil; mmiyakoshi at ucsd.edu; eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] ICA on lowpass / highpass filtered data

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  <mailto:mmiyakoshi at ucsd.edu>
<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  <mailto:martin.krebber at charite.de>
<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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