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

Matt Craddock matt.craddock at uni-leipzig.de
Wed Nov 7 13:44:33 CET 2012

On 06/11/2012 19:44, Arnaud Delorme wrote:
> 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

Dear all,

Uh oh. If PCA is off the table, what should be done if the data is not 
full rank? e.g. if it's in average reference or multiple channels are 
bridged etc. Is the bias introduced by PCA worse than trying to 
decompose a matrix which is not full rank as if it were full rank?


cfg.runica.pca  =rank(your_number_of_channels)

always set it to 1, and thus tell runica to reduce to only a single 
component? Or does setting it to 1 tell runica to detect rank-deficiency 
and suggest an appropriate reduction? I don't normally run ICA via 
fieldtrip, so am not sure how to tell it to detect the rank and reduce 
accordingly; EEGLAB at least asks you about this when it detects rank 
deficiency (or at least, should do - i've found it a little 
temperamental on this point, but this is the Fieldtrip list so...).

Dr. Matt Craddock

Post-doctoral researcher,
Institute of Psychology,
University of Leipzig,
Seeburgstr. 14-20,
04103 Leipzig, Germany
Phone: +49 341 973 95 44

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