[Eeglablist] Decay in frequency contents when ICA is applied

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Wed May 21 19:18:06 PDT 2014

Dear Haj,

> The mean peak amplitude of the ICs is dramatically lower than channels!
(there was no IC rejection).

This is not the right way to make the comparison between IC and channel
In channels, alpha is represented redundantly, while in the independent
component space it is represented maximally independently from any other

You may want use 'pvaftopo' plugin to see how scalp channel signals are
explained by ICs in the sense of variance. In the bast case, 60-80% of
variance of a single channel can be explained by a single IC (if the source
and the channel are located closely, and the task used efficiently activate
the source all the time...) If you do this process with band-pass filter at
alpha range, you can obtain the measure of how much alpha at channels are
contributed by an IC.


2014-05-21 8:38 GMT-07:00 Haj Morez <hajmorez at gmail.com>:

> I am using AMICA ICA for decomposing EEG signals and study behavior of the
> EEG signals before and after applying ICA.
> At the moment I am trying  to compare spectral content of raw EEG signals,
> with spectral content of the ICA decomposition.
> I am using the "spectopo()" function from EEGLAB, and calculate the peak
> amplitude at 10 Hz averaged over all channels. Then trying to compare it
> with peak amplitude at 10 Hz averaged over all components (ICs).
> I noticed, there is a dramatic decay in the spectral content of the signal
> when ICA is applied. The mean peak amplitude of the ICs is dramatically
> lower than channels! (there was no IC rejection).
> The 10 Hz peak amplitude averaged over channels is ranging between 20-50
> dB, however it ranges between -20 to -5 dB when it is calculated over ICs.
> I really appreciate it if you could help me to explain the reason why it
> happens.
> P.S. I had same results when used EX-infomax ICA.
> Kindest regards,
> Haj
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Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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