ICA can be used in brain activation studies to reduce the number of dimension and filter out independent and interesting activations. This demonstration shows two studies. One provided by Hvidovre Universitets Hospital, Denmark, that consists of fMRI scannings of humans. Another provided by the EU sponsored MAPAWAMO project from fMRI scannings of monkeys. In the demo comparison between icaMS, icaML, icaMF, icaMF (positive sources) and pca can be made. More detailes can found in [2].
The Molgedey and Schuster decorrelation algorithm, having square mixing matrix and no noise . Truncation is used for the time shifted matrix, and it is forced to be symmetric . The delay Tau is estimated .
The number of independent components are calculated using Bayes Information Criterion (BIC), with pca for dimension reduction.
