1. Time-varying model identification for time–frequency feature extraction from EEG data
- Author
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Li, Yang, Wei, Hua-Liang, Billings, Stephen A., and Sarrigiannis, P.G.
- Subjects
- *
TIME-frequency analysis , *MATHEMATICAL models , *ESTIMATION theory , *SIGNAL processing , *WAVELETS (Mathematics) , *LEAST squares , *SIMULATION methods & models - Abstract
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. [Copyright &y& Elsevier]
- Published
- 2011
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