Parametric Modeling of EEG Signals

TL;DR

This paper introduces a parametric sinusoidal model for EEG signals that effectively captures their non-stationary, time-variant characteristics, enhancing analysis accuracy.

Contribution

A novel amplitude and frequency modulated sinusoidal model for EEG signals that accounts for their non-stationary and time-variant nature.

Findings

Model accurately captures EEG non-stationarity

Parameters are time-variant for longer signals

Improves EEG signal analysis accuracy

Abstract

In this paper, a new signal model is suggested for parametric representation of the electroencephalogram (EEG) signals. The proposed model which is an amplitude and frequency modulated sinusoidal signal model, has been found to capture the non-stationary characteristics of the EEG signal with good accuracy. When the EEG signal is considered for longer duration of time, the model parameters have turned to be time-variant.