We present a Bayesian filtering approach for automatic estimation of dynamical source models from MEG data. We apply multi-target Bayesian tracking and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set of dipolar sources. The reconstructed dipoles are clustered in time and space to associate them with sources. We applied this new method to synthetic data sets and show here that it is able to estimate the source structure more accurately than either traditional multi-dipole modeling or minimum current estimation performed by uninformed human operators. We also show that from real fields the method reconstructs a source constellation comparable to multi-dipole modeling

Dynamical MEG source modeling with multi-target bayesian tracking

SORRENTINO, ALBERTO;CAMPI, CRISTINA;PIANA, MICHELE
2009-01-01

Abstract

We present a Bayesian filtering approach for automatic estimation of dynamical source models from MEG data. We apply multi-target Bayesian tracking and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set of dipolar sources. The reconstructed dipoles are clustered in time and space to associate them with sources. We applied this new method to synthetic data sets and show here that it is able to estimate the source structure more accurately than either traditional multi-dipole modeling or minimum current estimation performed by uninformed human operators. We also show that from real fields the method reconstructs a source constellation comparable to multi-dipole modeling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/227991
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