Xuelin,
all of these questions are readily answered in the technical report
http://www.fmrib.ox.ac.uk/analysis/techrep/tr02cb1/tr02cb1.pdf
> 1. Is PCA applied to the data before ICA is applied? (to decrease
> the dimentionality)
Yes, the maximum likelihood estimate is given by a rotated PCA
solution (equation 5 in the TR)
> 2. If PCA is applied as a preprocessing, where are those principal
> components fed into?(put it in this way: After PCA applied, we have
> a matrix E of some eigenvectors, is E same with X? where X=MS, X:
> mixed information, M: mixing matrix, S: source)
Nope, PCA uses the Eigenvectors of the data covariance matrix of X,
see text after equation 5
> 3: What criterion is used to extract those ICs by MELODIC?
> (minimize the negentropy?)
Approximations to neg-entropy, see text and equation 12
> 4. What on earth are those ICs, which are extracted by MELODIC?
Projections of the data which are maximally non-Gaussianity - see
text and equation 12. You might find the section on 'uniqueness'
interesting, too
hope this helps
Christian
> Thanks a lot in advance
>
> Xuelin
>
> ****************************************
> Xuelin Cui
> Department of Electrical Engineering
> University of Hawaii-Manoa
> Honolulu HI 96822
>
> Tel: 1-808-349-0983
> Email: [log in to unmask]
> ****************************************
--
Christian F. Beckmann
Oxford University Centre for Functional
Magnetic Resonance Imaging of the Brain,
John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
Email: [log in to unmask] - http://www.fmrib.ox.ac.uk/~beckmann/
Phone: +44(0)1865 222551 Fax: +44(0)1865 222717
|