Bayesian nonstationary autoregressive models for biomedical signal analysis

Cassidy, Michael J and Penny, William D ORCID: (2002) Bayesian nonstationary autoregressive models for biomedical signal analysis. IEEE Transactions on Biomedical Engineering, 49 (10). pp. 1142-1152. ISSN 0018-9294

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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.

Item Type: Article
Uncontrolled Keywords: algorithms,bayes theorem,computer simulation,electroencephalography,visual evoked potentials,humans,likelihood functions,linear models,statistical models,quality control,regression analysis,computer-assisted signal processing,time factors,comparative study
Faculty \ School: Faculty of Social Sciences > School of Psychology
UEA Research Groups: Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences
Depositing User: Pure Connector
Date Deposited: 23 Aug 2017 05:04
Last Modified: 19 Apr 2023 22:33
DOI: 10.1109/TBME.2002.803511

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