We adapted a Bayesian tracking algorithm called particle filtering for estimating multiple current dipoles from magnetoencephalographic measurements. This method can reconstruct temporally correlated as well as moving dipolar sources in a fully automatic way. Here, we introduce the method and demonstrate its performance by modelling the highly correlated bilateral sources underlying the N100 auditory evoked response.
Particle filters: a new method for reconstructing multiple current dipoles from meg data
SORRENTINO, ALBERTO;PIANA, MICHELE
2007-01-01
Abstract
We adapted a Bayesian tracking algorithm called particle filtering for estimating multiple current dipoles from magnetoencephalographic measurements. This method can reconstruct temporally correlated as well as moving dipolar sources in a fully automatic way. Here, we introduce the method and demonstrate its performance by modelling the highly correlated bilateral sources underlying the N100 auditory evoked response.
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