Nonlinear analysis of EEG signals based on the parametric bispectral estimation

Abstract

In order to investigate the nonlinear relations of the electroencephalogram (EEG) signals under different brain functional states, higher-order statistics is used to study the nonlinear interrelation of the EEG components for the purpose of further understanding of the EEG generation and its construction. A parametric bispectral estimation for the analysis of EEG signals has been presented as an useful tool for detecting the nonlinearity of EEG signals. The bicoherence pattern is proposed in the paper to extract more. information beyond first and second-order statistics or spectral structure. Several EEG signals with normal subjects in different brain functional states are investigated by employing the non-Gaussian parametric model. The experimental results demonstrate that practical EEG signals provide obvious quadratic nonlinear coupling phenomena. The bicoherence structures of EEG signals is also different from that corresponding to the brain functional states. It is suggest that the bispectral analysis can be used as an effective way for nonlinear analysis and automatic classification of EEG signals and other biomedical measurements.