[FieldTrip] ICA subspace reduction with PCA eigenvalues
polomacnenad at gmail.com
Fri Nov 14 13:18:17 CET 2014
I have one question regarding ICA calculation subspace. I have high pass
filtered MEG data (> 30 Hz) on which I calculated ICA.
I know there are different methods to limit data dimensionality by ICA
calculation, usually performed by PCA prior to ICA. E.g. taking 99% of
variance explained or selecting first N components...
However I did it by taking specific eigenvalue (2.5^27) and assuming that
all components which have eigenvalue smaller than this represented noise.
Argument for this would be that eigenvalue of one component represent scale
of the variance explained by this component (for example, muscle artifacts
component would have high eigenvalues while noise component oscillate in
narrower range, hence variance and eigenvalue would be smaller). I have found
this value to result in a stable number of components of around 45 per
participant which is large enough to contain most of the signal and small
enough to obtain stable and meaningful ICA components.
Does anybody know any studies that used this constant eigenvalue approach
to limit ICA subspace?
Thank you very much in advance?
all the best!
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