Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: A comparison of algorithms

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. © 2010 American Institute of Mathematical Sciences.