ICA and artifact rejection

[Floris de Lange]

Dear Fieldtrippers,

I've started to play with using ICA to remove artifacts from the MEG
signal. Now, there's a couple of questions that came to my mind. I hope
anyone of you can help me with them.

1) ICA can be pretty slow, and it has been suggested in the Fieldtrip
mailing list to use PCA and estimate only the 50 components that explain
most variance. However, when I do this to e.g. identify ECG-related
artifacts, I run into trouble when trying to decompose the ECG-locked
data into the components, using the previously estimated 50 components
(in order to calculate the coherence between the components and the ECG
signal). The ECG-locked data is 275 channels X number of time points, so
it cannot be simply decomposed into 50 components. Does anyone know how
to go about this?
2) For EOG I suppose I could do like for ECG, and calculate the
coherence with VEOG in order to select components that are artifacts.
But this is not so easy for head muscle-related artifacts (jaw and neck
movements, etc.), since we typically don't measure neck and jaw muscle
EMG. Apart from looking at spatial topography, is there a robust
criterion that one could use for detection of EMG artifact components?
Or would it be better to use 'classical' artifact rejection routines for
these kinds of artifacts? And would ICA also be suitable to remove
things like jumps and head movements in the MEG, or is it better/safer
to just throw away those kind of data segments?

Thanks for your help,
Best wishes,
Floris

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