[FieldTrip] SVD and ICA

Federico Grande fgrande at cbs.mpg.de
Fri Jan 10 18:14:28 CET 2014

Aham, that is what I've done, do the runica method, but I didn´t use the parameter pca: pca are not principal component analysis associated to SSP? I have used SSS (signal space separation) instead of SSP. It would work also? And also I don´t know what number of components do I want to reduce my data. How can I know which is the optimal number?

Thank you Diego,


----- Original Message -----
From: "Lozano Soldevilla, D. (Diego)" <d.lozanosoldevilla at fcdonders.ru.nl>
To: "FieldTrip discussion list" <fieldtrip at science.ru.nl>
Sent: Friday, January 10, 2014 4:13:13 PM
Subject: Re: [FieldTrip] SVD and ICA

Hi Federico,

You might want to have a look to the different ICA algorithms ft_componentanalysis has and see how to choose the proper option. For example, if you select cfg.method='runica' then cfg.runica.pca = number of components you want to reduce your data.

Check help ft_componentanalysis for details


----- Original Message -----
> From: "Federico Grande" <fgrande at cbs.mpg.de>
> To: fieldtrip at science.ru.nl
> Sent: Friday, 10 January, 2014 3:54:56 PM
> Subject: [FieldTrip] SVD and ICA
> Hello everyone,
> In order to remove the artefacts like blink eyes or hearbeat, I wanted
> to apply ICA to my data. I've been told that is better to apply first
> SVD and then ICA, but I don't really know how to apply it. What do you
> recommend me in order to do it? I've not found any tutorial for doing
> it. All help and information ins greatly welcomed.
> Thank you very much,
> King Regards,
> Federico Grande
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip

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
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