A New Composite ICA Algorithm and Its Application in fMRI Data Processing

摘要

Independent Component Analysis (ICA) is a new technique in signal processing to extract statistically independent components from an observed multidimensional mixture of data. In this paper, a composite algorithm of Newton iteration and natural gradient descent (CNN) is presented to implement ICA by maximizing the sum of marginal Negentropies which is equivalen to minimizing the mutual information of independent signals. CNN algorithm avoids the singularity of Newton iteration. And at the same time it possesses higher convergence speed than the natural gradient algorithm. We specifically applied the algorithm to functional magnetic resonance imaging (fMRJ) data, and the results lend validity to the proposed method as providing a reasonable physiological explanation for the fMRI data.