Brain Source Localization for Two Dipoles Using a Combined Method from 32-Channel EEGs

A problem of estimating biopotential sources in the brain based on electroencephalographic (EEG) signals observed on the scalp is known as an important inverse problem of electrophysiology. Specifically, for the inverse problem from EEG data to dipole source there is no unique solution and solutions do not depend continuously on the data. In this paper we describe a system to localize two dipoles to reasonable accuracy from noisy EEG measurements generated by simulating calculation. The system combines backpropagation neural network (BPNN) with nonlinear least square (NLS) method for brain source localization. For inverse problem, the BPNN and the NLS method has its own advantage and disadvantage, so we use the BPNN to supply the initial value to the NLS method and then get the final result, here we select the Powell algorithm for the NLS calculating. All these work are for the fast and accurate dipole source localization. The following investigations are presented to show that this combined method used in this paper is an advanced approach for two dipole sources localization with high accuracy and fast calculating