Open AccessDynamical MEG source modeling with multi‐target Bayesian filtering
Author(s) -
Sorrentino Alberto,
Parkkonen Lauri,
Pascarella Annalisa,
Campi Cristina,
Piana Michele
Publication year - 2009
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20786
Subject(s) - magnetoencephalography , bayesian probability , computer science , artificial intelligence , set (abstract data type) , pattern recognition (psychology) , algorithm , dipole , bayesian inference , hum , data set , physics , electroencephalography , neuroscience , psychology , art , quantum mechanics , performance art , art history , programming language
Abstract We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. We apply multi‐target Bayesian filtering and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set of dipolar sources. The reconstructed dipoles are clustered in time and space to associate them with sources. We applied this new method to synthetic data sets and show here that it is able to automatically estimate the source structure in most cases more accurately than either traditional multi‐dipole modeling or minimum current estimation performed by uninformed human operators. We also show that from real somatosensory evoked fields the method reconstructs a source constellation comparable to that obtained by multi‐dipole modeling. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.