Source extraction from functional magnetic resonance imaging data using ICA based on fourth-order and exponential contrast functions

Abstract

Independent component analysis (ICA) is one of the well-known statistical techniques used for blind source separation. It is also used for the extraction of sources from functional magnetic resonance imaging (fMRI) data. Benchmark for different ICA algorithms is speed and accuracy. In this article, we will be focusing on two simple contrast functions along with matrix-based updating rules. Fixed-point iteration is used for optimization of the contrast functions. Application of matrix-based weight updating makes the process converge rapidly. Validity of the algorithms is tested by comparing the speed and accuracy on simulated and actual fMRI data with other conventional ICA approaches.