Spectral Estimation of Electroencephalogram Signal using ARMAX Model and Particle Swarm Optimization

Abstract

Electroencephalogram (EEG) signal is short-length time series data. Parametric and non-parametric methods are used to estimate the power spectral density (PSD) of the time series data. While the non-parametric approach of spectrum estimate of short-length data is unreliable, the parametric approach of spectrum estimate is widely suggested. In the presented work, a parametric autoregressive moving average with exogenous input (ARMAX) model was implemented to find the PSD of the EEG signal. The performance of the implemented ARMAX model was contrasted with other parametric methods like autoregressive AR) and autoregressive moving average (ARMA) and nonparametric methods like periodogram.

Coefficients of ARMAX and (ARMA) were estimated using the particle swarm optimization (PSO) technique based on swarm intelligence. The PSO algorithm adapted with the system identification technique and statistics yielded highly satisfactory results in finding the coefficients of the ARMAX model. All the programming and visualization were performed in a MATLAB environment.