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Research Article
Dynamical MEG source modeling with multi-target Bayesian filtering
Article first published online: 17 APR 2009
DOI: 10.1002/hbm.20786
Copyright © 2009 Wiley-Liss, Inc.
Issue
Human Brain Mapping
Special Issue: Electromagnetic Brain Imaging
Volume 30, Issue 6, pages 1911–1921, June 2009
Additional Information
How to Cite
Sorrentino, A., Parkkonen, L., Pascarella, A., Campi, C. and Piana, M. (2009), Dynamical MEG source modeling with multi-target Bayesian filtering. Hum. Brain Mapp., 30: 1911–1921. doi: 10.1002/hbm.20786
Publication History
- Issue published online: 11 MAY 2009
- Article first published online: 17 APR 2009
- Manuscript Accepted: 25 FEB 2009
- Manuscript Revised: 11 FEB 2009
- Manuscript Received: 31 OCT 2008
Funded by
- Academy of Finland (National Centers of Excellence Programme 2006-2011) and Fondazione CaRiVerona
- Abstract
- Article
- References
- Cited By
Keywords:
- magnetoencephalography (MEG);
- random finite sets;
- particle filter;
- Bayesian filtering;
- source localization;
- inverse problem
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.