document: Bayesian nonstationary autoregressive models for biomedical signal analysis 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
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