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Bayesian nonstationary autoregressive models for biomedical signal analysis

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2 Author(s)
M. J. Cassidy ; Sobell Dept. of Neurophysiol., Univ. Coll. London, UK ; W. D. Penny

We describe a variational Bayesian algorithm for the estimation of a multivariate autoregressive model with time-varying coefficients that adapt according to a linear dynamical system. The algorithm allows for time and frequency domain characterization of nonstationary multivariate signals and is especially suited to the analysis of event-related data. Results are presented on synthetic data and real electroencephalogram data recorded in event-related desynchronization and photic synchronization scenarios.

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:49 ,  Issue: 10 )