Dynamic solution to the EEG source localization problem using kalman filters and particle filters

In this paper, we propose a solution to the EEG source localization problem considering its dynamic behavior. We assume a dipolar approach which makes the problem nonlinear. From the dynamic probabilistic model of the problem, we formulate the extended Kalman filter and particle filter solutions. In order to test the algorithms, we designed an experimental protocol based on error-related potentials. During the experiments, our dynamic solutions have allowed the estimation of sources which are varying in position and moment within the brain volume. Results confirm the activation of the anterior cingulate cortex which is the brain structure associated with error processing. These findings demonstrate the good performance of the dynamic solutions for estimating and tracking EEG neural generators.