[Eeglablist] Problem with ICA decomposition

[Tarik S Bel-Bahar]

Hello Antonio, You should have enough data to make ICA happy, many groups
use much less time and few channels but get valid-enough artifact-ICs. Your
issue might be with rereferencing and adding CZ, and perhaps not fixing the
rank. If you haven't had a chance to, please review Makoto's pipeline
mentioned in the eeglablist, and the online eeglab tutorial. Googling
eeglablist may also help. Some other notes are listed below.

***************

remember to remove 1 channel to reduce rank for ICA, to reflect reduced
rank due to the average rereferencing. Please google eeglablist for past
mentions of this topic.

Yes initial steps should take a long time, in general. It does not seem
like it's an issue with your computer or installation.

consider downsampling the signal, or taking just half of the total signal
time. This should confirm the speedups you expect.

consider going to 1-50 hz initially, this should "catch" any stereotyped
components ICA can "see" in the data.

consider demeaning and/or detrending the data

consider detect+remove bad channels and not interpolating them before
average rereferencing
[try to make sure you end up with at least 25 channels and that they are
well distributed across the scalp.]

consider the Cz in via interpolation before average rereferencing [or just
leave out for now and interpolate it in after ICA-cleaning). In or out,  it
should not make much of difference in the hunt for ICA-derived stereotyped
artifacts.








On Wed, Jul 20, 2016 at 4:42 AM, Antonio Maffei <antonio.maffei at phd.unipd.it
> wrote:

> Dear all,
>
> I am stepping in some problems when running ICA decomposition for artifact
> detection.
>
> My dataset consists in a continous 38 channels 70 minutes long recording,
> sampled at 500 Hz referenced to Cz.
>
> My preprocessing steps are the following:
> - Re-reference to the average reference and adding Cz to the recording
> -Filter with a band-pass filter set at 1 - 100 Hz
> - Visual inspection of the recording and removal of big noisy artifacts,
> mainly movement artifacts, as suggested in the EEGLAB tutorials
>
> After these steps my dataset consists of 1864585 data points on which I
> perform *runica* with the default options ('extended', '1').
>
> I noticed that the process is very slow, and the algorithm needs to
> lowering the learning rate many times at the beginning but even so it seems
> that it fails to converge, since the wchange values does not decrease
> progressively (as they should) and it fails to reach the stop criterium
> (wchange <1e-07).
>
> As a consequence I get a bad decomposition with uninterpretable components
> that prevent their use for artifact correction.
>
> I am wondering if this problem is related to the amount of data points fed
> to the ICA, since when I preprocessed shorter recordings I have not
> encountered such difficulties, or I am making some mistakes during my
> pipeline.
>
> A great thank to anyone who can help me.
>
> Antonio
>
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