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

Higher-order statistics is applied to the analysis of electroencephalogram (EEG) in order to investigate the non-Gaussianility and nonlinearity of EEG signals. The parametric bispectral estimation is proposed in the paper for the purpose of extracting more information beyond second order statistics or power spectra. The actual EEG with normal subjects in several different functional states of the brain are analyzed in terms of the parametric bispectral estimation. 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 provides an effective quantitative measure for analyzing and processing electroencephalography in different physiological states of the brain.