Improvement of source localization by dynamical systems based modeling (DSBM)

Recently, we have proposed a new concept for analyzing EEG/MEG data (Uhl et al. 1998), which leads to a dynamical systems based modeling (DSBM) of neu rophysiological data. We report the application of this approach to four di fferent classes of simulated noisy data sets, to investigate the impact of DSBM-filtering on source localization. An improvement is demonstrated of up to above 50% of the distance between simulated and estimated dipole positi ons compared to principal component filtered and unfiltered data. On a nois e level on which two underlying dipoles cannot be resolved from the unfilte red data, DSBM allows for an extraction of the two sources.