A Modified Infomax ICA Algorithm for fMRI Data Source Separation

This study presents a modified infomax model of Independent Component Analysis (ICA) for the source separation problem of fMRI data. Functional MRI data is processed by different blind source separation techniques including Independent Component Analysis (ICA). ICA is a statistical decomposition method used for multivariate data source separation. ICA algorithm is based on independence of extracted sources for which different techniques are used like kurtosis, negentropy, information maximization etc. The infomax method of ICA extracts unknown sources from a number of mixtures by maximizing the negentropy thus ensuring independence. In this proposed modified infomax model a higher order contrast function is used which results in fast convergence and accuracy. The Proposed algorithm is applied to general simulated signals and simulated fMRI data. Comparison of correlation results of the proposed algorithm with the conventional infomax algorithm shows better performance.