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Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets

Published Online:pp 215-224

A new time-varying autoregressive (TVAR) modelling approach is proposed for non-stationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multiwavelet decomposition scheme. The time-varying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method.


time-varying modelling, autoregressive models, TVAR modelling, system identification, model structure detection, orthogonal least squares, OLS, time-dependent spectra, wavelets, EEG data modelling, electroencephalography, power spectral estimation, medical diagnosis, clinical neurophysiology, brain function, cognitive neuroscience, medical imaging