  In this paper, we propose a novel approach for parametric modeling of
electroencephalographic (EEG) signals. It is demonstrated that the EEG signal
is a mono-component non-stationary signal whose amplitude and phase (frequency)
can be expressed as functions of time. We present detailed strategy for
estimation of the parameters of the proposed model with high accuracy.
Simulation study illustrates the procedure of model fitting. Some
interpretation of the characteristic features of the model is described.
