[FieldTrip] ICA on MEG data

[Kathrin Müsch]

Dear Irina,

I reduced the number of components to 64 by running a PCA beforehand (cfg.runica.pca = 64) on my MEG data. Depending on how much the subjects moved their eyes, blinks and saccades popped up in earlier or later components. I had feasible results with this approach both for EOG and ECG.

Good luck,
Kathrin
_____________________________________
Kathrin Müsch

Dept. of Neurophysiology and Pathophysiology
University Medical Center Hamburg-Eppendorf
Martinistr. 52
20246 Hamburg
Germany
Phone: +49-40-7410-54680
Fax: +49-40-7410-57752
E-Mail: k.muesch at uke.uni-hamburg.de
_____________________________________

Am 30.08.2011 um 10:15 schrieb Stan van Pelt:

> Dear Irina,
> 
> The number of time points should indeed be much larger for a good ICA
> estimate. However, that seems to be the case with your data, so I think
> there's no problem there. 
> 
> As far as I know, with ICA in fieldtrip you always get the same amount of
> output components as input channels. Activity in adjacent MEG channels
> will be somewhat correlated indeed, but not nearly as much as with EEG.
> Moreover, the origin of your artifacts (EOG, maybe also ECG) will create a
> distinct topographic distribution, and the components will pop out clearly
> from your ft_componentanalysis, because it explains large amounts of
> variance in the data.
> 
> Just give it a try, I'd say. You should get similar results as in the Wiki
> example.
> 
> Best,
> Stan
> 
> 
> -----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 9:56 AM
> To: Email discussion list for the FieldTrip project
> Subject: Re: [FieldTrip] ICA on MEG data
> 
> Dear Stan,
> 
> Thank you for your reply,
> 
> As far as I understand from the tutorials about ICA by Scott Makeig
> http://sccn.ucsd.edu/~scott/tutorial/icafaq.html 
> , the number of time points should be considerably larger than the  
> number of scalp sensors. How critical is this point?
> 
> Besides, the sensors are very close to each other, and the registered  
> activity in adjacent sensors is highly correlated, so the components  
> would be similar, right? Isn't it reasonable to reduce the number of  
> output components then?
> 
> Thank you in advance,
> Irina
> 
> 
> 
> On Aug 30, 2011, at 9:14 AM 8/30/11, Stan van Pelt wrote:
> 
>> 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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