Particle filtering, beamforming and multiple signal classification for the analysis of
magnetoencephalography time series: a comparison of algorithms
Annalisa Pascarella - Dipartimento di Matematica, Università di Genova, via Dodecaneso 35 16146 Genova, Italy (email)
Abstract: We present a comparison of three methods for the solution of the magnetoencephalography inverse problem. The methods are: a linearly constrained minimum variance beamformer, an algorithm implementing multiple signal classification with recursively applied projection and a particle filter for Bayesian tracking. Synthetic data with neurophysiological significance are analyzed by the three methods to recover position, orientation and amplitude of the active sources. Finally, a real data set evoked by a simple auditory stimulus is considered.
Keywords: Inverse problems, magnetoencephalography, Bayesian methods.
Received: October 2008; Revised: December 2009; Available Online: February 2010.
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