Higher-order spectrum in understanding nonlinearity in EEG rhythms
type: Item
instance of: scholarly article
title: Higher-order spectrum in understanding nonlinearity in EEG rhythms
zbMATH Open document ID: 1233.92053
DOI: 10.1155/2012/206857
author: D. Kharzeev
published in: Computational \& Mathematical Methods in Medicine
publication date: 2012-03-13T00:00:00Z
review text: Summary: The fundamental nature of the brain's electrical activities recorded as electroencephalograms (EEG) remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEGs because of their robustness, simplicity of interpretation, and apparent association with the rhythmic behavioral patterns in nature. We extend the use of a higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extended Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, and therefore can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. This paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs) and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows the utility of bispectral methods as an analytical tool in understanding the neural processes underlying human EEG patterns.
Mathematics Subject Classification ID: 92C55
Mathematics Subject Classification ID: 92C20
Mathematics Subject Classification ID: 65T99
Mathematics Subject Classification ID: 78A70
Mathematics Subject Classification ID: 37D45
zbMATH DE Number: 6014115
Wikidata QID: Q30469751
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/206857
OpenAlex ID: W2022968024
cites work: A PRACTICAL METHOD FOR THE MEASUREMENTS OF THE CHAOTICITY OF ELECTRIC AND MAGNETIC BRAIN ACTIVITY
cites work: Cepstrum of bispectrum-a new approach to blind system reconstruction
cites work: Testing nonstationary time series for Gaussianity and linearity using the evolutionary bispectrum: an application to internet traffic data
DBLP publication ID: journals/cmmm/PradhanJNP12
description: scientific article; zbMATH DE number 6014115