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Local polynomial modelling of time-varying autoregressive processes and its application to the analysis of event-related electroencephalogram

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3 Author(s)
Z. G. Zhang ; Department of Electrical and Electronic Engineering The University of Hong Kong, Pokfulam Road, Hong Kong ; S. C Chan ; Y. S. Hung

This paper proposes a new method for identification of time-varying autoregressive (TVAR) models based on local polynomial modeling (LPM) and applies it to investigate the dynamic spectral information of event-related electroencephalogram (EEG). The proposed method models the TVAR coefficients locally by polynomials and estimates those using least-squares estimation with a kernel having a certain bandwidth. A data-driven variable bandwidth selection method is developed to obtain the optimal bandwidth, which minimizes the mean squared error (MSE). Simulation results show that the LPM-based TVAR identification method outperforms conventional methods for different scenarios. The advantages of the LPM method make it a useful high-resolution time-frequency analysis (TFA) technique for nonstationary biomedical signals like EEG. Experimental results show that the LPM method can reveal more meaningful time-frequency characteristics than wavelet transform.

Published in:

Proceedings of 2010 IEEE International Symposium on Circuits and Systems

Date of Conference:

May 30 2010-June 2 2010