Application of Kalman Filter for the estimation of human head tissue conductivities

In this study Extended Kalman Filtering is proposed for the estimation of human head tissue conductivities by using EEG data. The proposed method first linearizes the relationship between the tissue conductivities and surface potentials (EEG measurements) and then iteratively estimates the tissue conductivities. In the study the mathematical background of the proposed method is presented and then performance of the proposed method is investigated by a simulation study. In the simulation study a three layered realistic head model (composed of scalp, skull and brain compartments) obtained from MR images of a real patient is used. The surface potential is calculated by using an arbitrarily chosen conductivity distribution. Then conductivity estimation is iteratively performed by using the calculated potentials and at each iteration relative error rates are calculated by comparing the orginal conductivities and estimated ones. It is found that the relative error rates decrease below of 1% after five iterations.