title: Time-varying model identification for time-frequency feature extraction from EEG data.
abstract: A novel modelling scheme that can be used to estimate and track time-varying properties of nonstationary signals is investigated. This scheme is based on a class of time-varying AutoRegressive with an eXogenous input (TVARX) models where the associated time-varying parameters are represented by multi-wavelet basis functions. The orthogonal least square (OLS) algorithm is then applied to refine the model parameter estimates of the TVARX model. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. Simulation studies and applications to real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of nonstationary processes.
authors.affiliation: Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapping Street, Sheffield S1 3JD, UK. coq08yl@sheffield.ac.uk
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.forename: Hua-Liang
authors.initials: HL
authors.lastname: Wei
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Billings
authors.forename: P G
authors.initials: PG
authors.lastname: Sarrigiannis
mesh_terms: D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography; D006801:Humans; D008959:Models, Neurological; D013997:Time Factors