Title :
Application of higher-order statistics for the analysis of electroencephalogram in different brain functional states
Author :
Minfen, Shen ; Lisha, Sun ; Congtao, Xu ; Guoping, Zhu
Author_Institution :
Dept. of Sci. Res., Shantou Univ., Guangdong, China
Abstract :
Higher-order statistics are applied to the analysis of electroencephalograms (EEGs) in order to investigate their non-Gaussianility and nonlinearity. Parametric bispectral estimation is proposed in this paper for the purpose of extracting more information, beyond second-order statistics or power spectra. The EEGs of normal subjects in different brain functional states are analyzed in terms of bispectral estimation. The experimental results show that all kinds of EEGs exhibit obvious quadratic nonlinear interactions, but the bispectral structure of a normal EEG changes with different functional states of the brain. It is suggested that the bispectrum could be regarded as one of the main characteristics in the study of EEG signals
Keywords :
electroencephalography; higher order statistics; medical signal processing; parameter estimation; spectral analysis; EEG nonGaussianility; EEG nonlinearity; EEG signal analysis; brain functional states; electroencephalogram; higher-order statistics; information extraction; parametric bispectral estimation; power spectra; quadratic nonlinear interactions; Data mining; Electroencephalography; Gaussian processes; Higher order statistics; Information analysis; Parametric statistics; Signal analysis; Signal processing; Spectral analysis; State estimation;
Conference_Titel :
Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-5871-6
DOI :
10.1109/ICONIP.1999.845666
