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.
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