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 neurophysiological data. We report the application of this approach to four different 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 positions compared to principal component filtered and unfiltered data. On a noise level on which two underlying dipoles cannot be resolved from the unfiltered data, DSBM allows for an extraction of the two sources.
Uhl
C
C
Max-Planck-Institute of Cognitive Neuroscience, Leipzig, Germany.
Hutt
A
A
Kruggel
F
F
D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D008959:Models, Neurological