American Institute of Mathematical Sciences

Advanced
February  2010, 4(1): 169-190. doi: 10.3934/ipi.2010.4.169

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

Annalisa Pascarella 1, , Alberto Sorrentino 2, , Cristina Campi 1, and  Michele Piana 1,

1. 

Dipartimento di Matematica, Università di Genova, via Dodecaneso 35 16146 Genova, Italy, Italy, Italy
2. 

CNR - INFM LAMIA, via Dodecaneso 33 16146 Genova, Italy

Received  October 2008 Revised  December 2009 Published  February 2010

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: magnetoencephalography, Bayesian methods., Inverse problems.
Mathematics Subject Classification: Primary: 65R32; Secondary: 65C0.
Citation: Annalisa Pascarella, Alberto Sorrentino, Cristina Campi, Michele Piana. Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: a comparison of algorithms. Inverse Problems & Imaging, 2010, 4 (1) : 169-190. doi: 10.3934/ipi.2010.4.169
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