Statistical Analysis For FEM EEG Source Localization in Realistic Head Models
Leonid E. Zhukov, David M. Weinstein, Chris R. Johnson · 2000
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 dierent congurations of sources can generate identical external electric elds. In this paper we consider a spatio-temporal model, taking advantage of the entire EEG time series to reduce the extent of the conguration space we must evaluate. We apply the recently derived infomax 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 benet of our ICA preprocessing step is that we obtain an a priori estimation of the number of independent sources producing the measured signal.