[FieldTrip] ft_componentanalysis component timecourse

["Jörn M. Horschig"]

Dear Eliezer,

There seems to be some confusion. component.topo is actually NxM, where 
N is the number of sensors and M the number of components you computed. 
M is equal to N if your channels are independent from each other, thus 
your data is full rank (this may not be the case if there e.g. is a 
gel-bridge or channels did not record a signal - please correct me if I 
am wrong).
Anyway, as you pointed out, [quote] "component.trials" is numTrials x 
sensors x time [/quote], actually it is numTrials x components x time, 
which means you get one time course for each component per trial. 
components.topo contains the (un)mixing matrix, i.e. the weighting of 
the individual channels per component (that is probably what you mean, 
when you referred to 'components').
So, dependent on what you want, these are the two field of interest. If 
you want to know your components, you have to use component.topo, if you 
want to look at the time courses, use component.trials.

Hope this helps!
Best regards,
Jörn

On 3/4/2011 3:10 PM, Kanal Eliezer wrote:
> Hello  -
>
> I'm trying to use the ft_componentanalysis function to remove a motion artifact in my dataset, but I'm confused as to how this function works. If called as
>
> component = ft_componentanalysis(cfg, data),
>
> the output seems to be in two fields, component.topo and component.trials. However, neither of these seems to contain the actual components. "component.topo" holds some an NxN matrix, where N is the number of sensors; I would expect the components to be "N x time". "component.trials" is numTrials x sensors x time, which seems to represent many more components than were actually calculated. How do I get the actual component timecourses?
>
> Thanks,
> Eliezer
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-- 
Jörn M. Horschig
PhD Student
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognitive Neuroimaging
Radboud University Nijmegen

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