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

Pages: 169 - 190, Volume 4, Issue 1, February 2010

doi:10.3934/ipi.2010.4.169       Abstract        Full Text (1083.8K)       Related Articles

Annalisa Pascarella - Dipartimento di Matematica, Università di Genova, via Dodecaneso 35 16146 Genova, Italy (email)
Alberto Sorrentino - CNR - INFM LAMIA, via Dodecaneso 33 16146 Genova, Italy (email)
Cristina Campi - Dipartimento di Matematica, Università di Genova, via Dodecaneso 35 16146 Genova, Italy (email)
Michele Piana - 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.
Mathematics Subject Classification:  Primary: 65R32; Secondary: 65C05.

Received: October 2008;      Revised: December 2009;      Published: February 2010.