title: A review of parametric modelling techniques for EEG analysis.
abstract: This review provides an introduction to the use of parametric modelling techniques for time series analysis, and in particular the application of autoregressive modelling to the analysis of physiological signals such as the human electroencephalogram. The concept of signal stationarity is considered and, in the light of this, both adaptive models, and non-adaptive models employing fixed or adaptive segmentation, are discussed. For non-adaptive autoregressive models, the Yule-Walker equations are derived and the popular Levinson-Durbin and Burg algorithms are introduced. The interpretation of an autoregressive model as a recursive digital filter and its use in spectral estimation are considered, and the important issues of model stability and model complexity are discussed.
authors.affiliation: University of Oxford, Medical Engineering Unit, UK.
authors.forename: J
authors.initials: J
authors.lastname: Pardey
authors.forename: S
authors.initials: S
authors.lastname: Roberts
authors.forename: L
authors.initials: L
authors.lastname: Tarassenko
mesh_terms: D001698:Biomedical Engineering; D003201:Computers; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data; D006801:Humans; D008433:Mathematics; D008959:Models, Neurological