ICA based multiple brain sources localization

In this paper we describe an Independent Component Analysis (ICA) method for computing the brain signals of unknown source parameters for the inverse problem. First, a method is applied to estimate the number of dipoles beforehand and reduce dimensionality which can reduce the ICA complexity and improve the unmixing accuracy. We apply Blind Source Separation (BSS) for separating multichannel EEG evoked by multiple dipoles into temporally independent stationary sources. For every independent source, we are able to determine the electrode potentials evoked by every dipole separately by the projection of independent activation maps back onto the electrode arrays. Then for every set of electrode potentials, a source localization procedure is performed which only involves searching for one dipole, thus dramatically reducing the search complexity. In the paper, it is explored that the possibility of applying ICA for EEG multiple dipoles localization when the data are corrupted by additive noise. Furthermore, we also give the relationship of unmixing accuracy, distance between dipoles and dipoles moment movements.