[FieldTrip] ICA on MEG data

[Stan van Pelt]

Dear Irina,

There's a page on the Fieldtrip Wiki about using ICA to remove
EOG-artifacts, see
http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_i
ca_to_remove_eog_artifacts.

The common approach is to first define your trials, and then run ICA on
them.

You will get as many output components as you have channels, i.e. 275 in
your case. The EOG artifacts usually pop up in 1 or 2 components within
the first 20 largest components.

Best regards,
Stan

Stan van Pelt, PhD
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen 
Kapittelweg 29, 6525 EN Nijmegen, Netherlands
E-mail:  stan.vanpelt at donders.ru.nl 
Website: www.ru.nl/donders/
Phone:  (+31) (0)24 36 68495
Fax:    (+31) (0)24 36 10989

-----Original Message-----
From: fieldtrip-bounces at donders.ru.nl
[mailto:fieldtrip-bounces at donders.ru.nl] On Behalf Of Irina Simanova
Sent: Tuesday, August 30, 2011 8:03 AM
To: Email discussion list for the FieldTrip project
Subject: [FieldTrip] ICA on MEG data

Dear FieldTripers,

I would like to identify ocular artifacts in an MEG dataset using the  
ICA.

The data consist of 180 trials of variable duration ( the signal from  
stimulus onset to subject's response). The trials' duration vary from  
400 ms to 3000 ms. The data is sampled at 1.2 kHz, and has 275  
channels. I am new to ICA, and I need a piece of advice. What would be  
a reasonable number of output independent components?
Should I do the PCA as a pre-processing stage? How do i find an  
optimal number of principal components to extract?

I would appreciate your help,

Kind regards,

Irina Simanova
PhD student
Neurobiology of Language Group
Max-Planck Institute for Psycholinguistics
Wundtlaan 1
6525 XD Nijmegen
The Netherlands
e-mail: irina.simanova at mpi.nl
phone: +31 24 3521541

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