FACTOR ANALYSIS PREPROCESSING FOR ICA
Shiro Ikeda
shiro@brain.riken.go.jp
One of the reasons ICA (Independent Component
Analysis) became so popular is that ICA is a promising
tools for a lot of applications. One of the attractive ap
plications is the biological data analysis. There are a lot
of works on neurobiological data analysis such as EEG
(Electroencephalography), fMRI (functional Magnetic
Resonance Imaging), and MEG (Magnetoencephalog
raphy), and they show interesting results. However,
there still remain some problems to be solved. Many
neurobiological data includes a large amount of noises,
and also the number of independent components is un
known. These problems make it difficult to obtain good
results by ICA algorithms. We discuss an approach to
separate the data which contain additive noise without
knowing the number of independent components. Our
approach uses factor analysis as the preprocessing of
the ICA algorithm, instead of PCA (Principal Com
ponent Analysis), which is the major preprocessing in
many ICA algorithms. In the new preprocessing, the
number of the sources and the amount of sensor noise
are estimated. After the preprocessing, an ICA algo
rithm is used to estimate the separation matrix and
mixing system. Through the experiments with MEG
data and fMRI data, we show this approach is effective.