[FieldTrip] using cfg.runica.pca to reduce number of ICs

[Lozano Soldevilla, D. (Diego)]

Hi Fred,

I don't know the magical number but I see the following options:

a) Before ICA, concatenate all single trials and ask for the rank of your data. Use it as your cfg.runica.pca input.

b) Some users notice that when you sort IC by variance, beyond component 30, the IC topo/activation does not look like physiologically meaningfull. It matches with the PCA reduction before ICA with other papers:

http://www.ncbi.nlm.nih.gov/pubmed/15219593
http://www.ncbi.nlm.nih.gov/pubmed/19699307

Might be somebody in the forum have tried (by simulations or in real data) on the effects of PCA component reduction on ICA.

I hope it helps,

Diego

----- Original Message -----
> From: "Frédéric Roux" <f.roux at bcbl.eu>
> To: "FieldTrip discussion list" <fieldtrip at science.ru.nl>
> Sent: Tuesday, 8 April, 2014 2:46:22 PM
> Subject: [FieldTrip] using cfg.runica.pca to reduce number of ICs
> Dear all,
> 
> I have a general question relating to the usage of cfg.runica.pca
> during the call to ft_componentanalysis.
> 
> Is it correct to assume that cfg.runica.pca = length(meg_data.label)
> will
> force the algorithm to return n = length(meg_data.label) ICs, and that
> as a
> result artifacts can be "spread" across several ICs?
> 
> If that's true, then I imagine that cfg.runica.pac = n/4 will return
> less components
> and reduce the "spread" of artifacts over several components.
> 
> My question is how to choose the number of principal components to
> which the data
> is reduced before ICA?
> 
> Best,
> Fred
> ---------------------------------------------------------------------------
> 
> 
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-- 
PhD Student
Neuronal Oscillations Group
Donders Institute for Brain, Cognition and Behaviour 
Centre for Cognitive Neuroimaging
Radboud University Nijmegen 
NL-6525 EN Nijmegen
The Netherlands
http://www.ru.nl/people/donders/lozano-soldevilla-d/