Statistical analysis for FEM EEG source localization in realistic head models

Estimating the location and distribution of electric current sources within the brain from electroencephalographic (EEG) recordings is an ill-posed inverse problem. The ill-posed nature of the inverse EEG problem is due to the lack of a unique solution such that different configurations of sources can generate identical external electric fields. In this paper we consider a spatio-temporal model, taking advantage of the entire EEG time series to reduce the extent of the configuration space we must evaluate. We apply the recently derived informx algorithm for performing Independent Component Analysis (ICA) on the time-dependent EEG data. This algorithm separates multichannel EEG data into activation maps due to temporally independent stationary sources. For every activation map we perform a source localization procedure, looking only for a single dipole per map, thus dramatically reducing the search complexity. An added benefit of our ICA preprocessing step is the we obtain an a priori estimation of the number of independent sources producing the measured signal.