[Eeglablist] ICA on lowpass / highpass filtered data

[Makoto Miyakoshi]

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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