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

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 experimen tal results show that all kinds of spontaneous EEG exhibit obvious quadrati c nonlinear interactions of EEG signals, but the bispectral pattern of norm al EEG changes with different functional states of the brain. It is suggest ed that the bispectrum could be regarded as the main feature in the study o f EEG signals, and an effective quantitative measure for analysing and proc essing electroencephalography in different physiological states of the brai n is provided.