Parametric modelling of non-stationary signals: a unified approach
Signal Processing
A new technique to reduce cross terms in the Wigner distribution
Digital Signal Processing
Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition
Research Letters in Signal Processing
Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition
Computer Methods and Programs in Biomedicine
An SVM-AdaBoost facial expression recognition system
Applied Intelligence
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In this paper, we propose a second-order linear time-varying autoregressive (TVAR) process for parametric representation of the electroencephalogram (EEG) signals. The coefficients of the Fourier-Bessel (FB) series expansion have been used to constitute a feature vector for segmentation of the EEG signal. Our approach is novel in the sense that by selecting an appropriate data length, we find a simple model for parametric representation of the EEG signals. The complete method for estimation of model parameters is presented in this work.