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

MPG-Autoren
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Uhl,  Christian
Max Planck Research Group Neurocognition of Prosody, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Hutt,  A.
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Kruggel,  F.
Department Cognitive Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Zitation

Uhl, C., Hutt, A., & Kruggel, F. (2001). Improvement of source localization by dynamical systems based modeling (DSBM). Brain Topography, 13(3), 219-226. doi:10.1023/A:1007859220001.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0010-BC29-1
Zusammenfassung
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.