Shiro Ikeda

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.