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.
authors.affiliation: Sobell Department of Neurophysiology, Institute of Neurology, University College London, UK. m.cassidy@ion.ucl.ac.uk
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Cassidy
authors.forename: William D
authors.initials: WD
authors.lastname: Penny
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods* / Q000706:statistics & numerical data; D005074:Evoked Potentials, Visual / Q000502:physiology; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D011786:Quality Control; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors