Parametric bispectral estimation of EEG signals in different functional states of the brain

Author

Shen, M. ; Chan, F.H.Y. ; Sun, L. ; Beadle, F.J.

Author_Institution

Dept. of Sci. Res., Shantou Univ., Guangdong, China

Volume

147

Issue

6

fYear

2000

fDate

11/1/2000 12:00:00 AM

Firstpage

374

Lastpage

377

Abstract

Higher-order statistics is applied to the analysis of electroencephalograms (EEG) in order to investigate the non-Gaussianity and nonlinearity of EEG signals. The parametric bispectral estimate is proposed for the purpose of extracting more information beyond second order statistics. The actual EEGs, with normal subjects in several different functional states of the brain, are analysed in terms of the parametric bispectral estimate. The experimental results show that all kinds of spontaneous EEG exhibit obvious quadratic nonlinear interactions of EEG signals, but the bispectral pattern of normal EEG changes with different functional states of the brain. It is suggested that the bispectrum could be regarded as the main feature in the study of EEG signals, and an effective quantitative measure for analysing and processing electroencephalography in different physiological states of the brain is provided

Keywords

electroencephalography; medical signal processing; parameter estimation; spectral analysis; statistical analysis; EEG analysis; EEG processing; EEG signals; brain functional states; effective quantitative measure; electrodiagnostics; parametric bispectral estimation; quadratic nonlinear interactions; spontaneous EEG;

fLanguage

English

Journal_Title

Science, Measurement and Technology, IEE Proceedings -

Publisher

iet

ISSN

1350-2344

Type

jour

DOI

10.1049/ip-smt:20000847

Filename

899994