[FieldTrip] 'rule of thumb' for defining how many independent components to analyze for EEG?

Arnaud Delorme arno at cerco.ups-tlse.fr
Tue Nov 6 19:44:03 CET 2012


Dear Ellie,

you should decompose your full rank matrix. PCA dimension reduction may seriously affects and bias ICA solutions (I have seen a yet-to-be published report on that).

Best,

Arno

On 6 Nov 2012, at 08:31, Eleanor Harding wrote:

> Dear Fieldtrip community,
> 
> I intend to run ICA on an EEG dataset with 64 channels (+ 2 eye channels), on epochs which are about 4 seconds long, and I am unsure as to how many independent components I should define (20,65,..?).
> 
> My question is, is there a 'rule of thumb' for defining how many independent components to analyze for EEG? In the archives I have found only references to MEG datasets with many channels, for example reducing over 200 channels to 80 components. Looking in the literature today I also wasn't able to interpret an answer.
> 
> If anyone has any information, references, or other input I would be much obliged.
> 
> Thanks,
> Ellie
> 
> 
> 
> -- 
> ------------------------------------------------------------------ 
> Eleanor Harding 
> PhD Student 
> Max Planck Institute for Human Cognitive and Brain Sciences 
> Stephanstra├če 1A, 04103 Leipzig, Germany 
> Phone: +49 341 9940-2268 
> Fax: +49 341 9940 2260 
> http://www.cbs.mpg.de/~harding 
> 
> 
> 
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