Parametric Modeling of EEG by Mono-Component Non-Stationary Signal

TL;DR

This paper introduces a new parametric model for EEG signals, treating them as mono-component non-stationary signals with time-varying amplitude and phase, and provides an accurate estimation method demonstrated through simulations.

Contribution

It presents a novel parametric modeling approach for EEG signals as mono-component non-stationary signals with a detailed parameter estimation strategy.

Findings

Model fitting demonstrated with simulation studies

High accuracy in parameter estimation achieved

Interpretation of characteristic features provided

Abstract

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.