Global optimal ICA and its application in MEG data analysis

This paper presents a new global optimal independent component analysis (ICA) algorithm. The constrained optimization problem, which is typically encountered with conventional ICA, is converted to an unconstrained problem with the orthogonal projection approach. Then the particle swarm optimization (PSO) is employed to solve the unconstrained problem and determine the separating matrix of ICA. Applications in the analysis of the magnetoencephalographic recordings (MEG) illustrate the efficiency of the proposed PSO–ICA approach.