Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG |
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Authors: | Zhang Z G Hung Y S Chan S C |
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Affiliation: | Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam, Hong Kong. zgzhang@eee.hku.hk |
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Abstract: | This paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error. The resultant time-varying power spectral density estimation of the signal is capable of achieving both high time resolution and high frequency resolution in the time-frequency domain, making it a powerful TFA technique for nonstationary biomedical signals like ER-EEG. Experimental results on synthesized signals and real EEG data show that the LPM method can achieve a more accurate and complete time-frequency representation of the signal. |
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