Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG.
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.
Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam, Hong Kong. zgzhang@eee.hku.hk
Z G
ZG
Zhang
Y S
YS
Hung
S C
SC
Chan
D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D008962:Models, Theoretical; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted