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

arXiv: https://arxiv.org/abs/2006.15911 · HTML: https://mlsanity.com/paper/2006.15911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Detecting high-frequency brain disorder signals using dynamic mode decomposition from EEG](https://mlsanity.com/paper/2608.02804/) (2026)

## Abstract (arXiv)

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.