Time-varying model identification for time-frequency feature extraction from EEG data
Li, Y., Wei, H.L., Billings, S.A. et al. (1 more author)
(2010)
Time-varying model identification for time-frequency feature extraction from EEG data.
Research Report.
ACSE Research Report no. 1021
.
Automatic Control and Systems Engineering, University of Sheffield
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 (ARX) 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 time-varying ARX 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.
Metadata
Authors/Creators:
Li, Y.
Wei, H.L.
Billings, S.A.
Sarrigiannis, P.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
Keywords:
TVARX model, wavelet basis functions, model structure detection, recursive least squares (RLS), orthogonal least square (OLS), time-dependent spectra, EEG.
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