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Local Polynomial Modeling of Time-Varying Autoregressive Models With Application to Time–Frequency Analysis of Event-Related EEG

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3 Author(s)
Zhang, Z.G. ; Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China ; Hung, Y.S. ; Chan, S.C.

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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Biomedical Engineering, IEEE Transactions on  (Volume:58 ,  Issue: 3 )