Bayesian nonstationary autoregressive models for biomedical signal analysis

Michael J Cassidy; William D Penny

doi:10.1109/TBME.2002.803511

Bayesian nonstationary autoregressive models for biomedical signal analysis

Michael J Cassidy, William D Penny

Research output: Contribution to journal › Article › peer-review

49 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1142-1152
Number of pages	11
Journal	IEEE Transactions on Biomedical Engineering
Volume	49
Issue number	10
DOIs	https://doi.org/10.1109/TBME.2002.803511
Publication status	Published - Oct 2002

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

Access to Document

10.1109/TBME.2002.803511

Cite this

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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.",

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",

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AB - 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.

KW - Algorithms

KW - Bayes Theorem

KW - Computer Simulation

KW - Electroencephalography

KW - Visual Evoked Potentials

KW - Humans

KW - Likelihood Functions

KW - Linear Models

KW - Statistical Models

KW - Quality Control

KW - Regression Analysis

KW - Computer-Assisted Signal Processing

KW - Time Factors

KW - Comparative Study

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