[Eeglablist] Problem with ICA decomposition

[Antonio Maffei]

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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20160720/13ecb685/attachment.html>