Title
Development of a variational Bayesian expectation maximization (VBEM) method for model inversion of multi-area E/MEG model
Abstract
We develop and evaluate a variational Bayesian expectation maximization (VBEM) method for model inversion of our multi-area extended neural mass model (MEN) using EEG/MEG data. Parameters of MEN have suitable prior distributions that enable us to use properties of a conjugate-exponential model in implementing VBEM. Consequently, VBEM leads to analytically tractable forms that starts with initialization and consists of repeated iterations of a variational Bayesian expectation step (VB E-step) and a variational Bayesian maximization step (VB M-step). Posterior distributions of model parameters are updated in the VB M-step. Distribution of the hidden state is updated in the VB E-step using variational extended Kalman smoother. We evaluate and validate performance of VBEM method for model inversion of MEN using simulation studies in various signal-to-noise ratios. The proposed approach provides a useful technique for analyzing effective connectivity using non-invasive EEG and MEG methods.
Year
DOI
Venue
2011
10.1109/ISBI.2011.5872563
Chicago, IL
Keywords
Field
DocType
belief networks,electroencephalography,expectation-maximisation algorithm,magnetoencephalography,medical computing,neurophysiology,physiological models,conjugate-exponential model,model inversion,multiarea extended neural mass model,noninvasive EEG methods,noninvasive MEG methods,signal-noise ratios,variational Bayesian expectation maximization method,variational extended Kalman smoother,EEG,MEG,Model Inversion,Variational Bayesian Expectation Maximization
Data modeling,Model inversion,Computer science,Artificial intelligence,Kalman smoother,Pattern recognition,Expectation–maximization algorithm,Signal-to-noise ratio,Algorithm,Initialization,Maximization,Machine learning,Bayesian probability
Conference
ISSN
ISBN
Citations 
1945-7928 E-ISBN : 978-1-4244-4128-0
978-1-4244-4128-0
1
PageRank 
References 
Authors
0.35
4
6