title: Bayesian nonstationary autoregressive models for biomedical signal analysis
abstract: 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
keyphrases: Bayesian nonstationary autoregressive models biomedical signal analysis variational Bayesian algorithm time-varying coefficients linear dynamical system frequency domain characterization event-related desynchronization photic synchronization scenarios time domain characterization Kalman smoother EEG analysis