Stochastic modeling of EEG rhythms with fractional Gaussian Noise

Abstract

This paper presents a novel approach to signal modeling for EEG signal rhythms. A new method of 3-stage DCT based multirate filterbank is proposed for the decomposition of EEG signals into brain rhythms: delta, theta, alpha, beta, and gamma rhythms. It is shown that theta, alpha, and gamma rhythms can be modeled as 1st order fractional Gaussian Noise (fGn), while the beta rhythms can be modeled as 2nd order fGn processes. These fGn processes are stationary random processes. Further, it is shown that the delta subband imbibes all the nonstationarity of EEG signals and can be modeled as a 1st order fractional Brownian motion (fBm) process. The modeling of subbands is characterized by Hurst exponent, estimated using maximum likelihood (ML) estimation method. The modeling approach has been tested on two public databases.