A fMRI data analysis method using a fast infomax-based ICA algorithm

Independent component analysis (ICA) is a new technique in signal processing to extract statistically independent components from the observed multidimensional mixture of data. In this field, many algorithms have been proposed. An infomax-based fast algorithm for ICA is proposed, using information maximum likelihood estimation with the Newton iterative algorithm. The algorithm is second-order convergent. We specifically applied the algorithm to functional magnetic resonance imaging (fMRI) data, and the result is positive. These results lend validity to the proposed method as providing a reasonable physiological explanation for the fMRI data.

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