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

[Frédéric Roux]

Hi Diego,

thanks for the quick reply.

When I compute the rank of the concatenated trials I get rank(concat1) = 202, which is the number of
channels that are in the data (planar gradiometers only). So in fact that number corresponds to the default
output returned by ft_componentanalysis.

Alos, I usually run ICA without PCA component reduction and can identify EOG and ECG quite easily by
eye-balling. But, I'd like to figure out what the advantages of PCA reduction are.

For instance, will reducing the number of ICs through PCA help to isolate better EOG and ECG components
or will the decomposition be the same the only difference being that the algorithm will run faster?

Best,

Fred

Frédéric Roux

----- Original Message -----
From: "Mauricio Antelis" <mauricio.antelis at gmail.com>
To: "Diego Lozano" <d.lozanosoldevilla at fcdonders.ru.nl>, "FieldTrip discussion list" <fieldtrip at science.ru.nl>
Sent: Tuesday, April 8, 2014 3:36:42 PM
Subject: Re: [FieldTrip] using cfg.runica.pca to reduce number of ICs



Roberto, 


Aqui algo sobre el cuentionamiento del numero de componentes para realizar ICA, sin embargo en nuestro caso, creo que no sera un parametro sensible ya que tenemos un numero bajo (21) de mediciones 


Saludos 


Mauricio 





On Tue, Apr 8, 2014 at 8:29 AM, Lozano Soldevilla, D. (Diego) < d.lozanosoldevilla at fcdonders.ru.nl > wrote: 


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/ 

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-- 

Javier M. Antelis 

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