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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sun Lisha Shen Minfen Xu Congtao |
| Copyright Year | 2002 |
| Description | Author affiliation: Dept. of Electron. Eng., Shantou Univ., Guangdong, China (Sun Lisha; Shen Minfen) |
| 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. |
| Sponsorship | Chinese Inst. Electron. IEEE Signal Process. Soc. IEEE URSI IEEE Beijing Sect. Nat. Natural Sci. Found. China China Committee for URSI IEEE Comput. Soc. Beijing Chapter IEEE SP Soc. Beijing Chapter |
| Starting Page | 251 |
| Ending Page | 254 |
| File Size | 244958 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780374886 |
| DOI | 10.1109/ICOSP.2002.1181039 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-08-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Signal analysis Electroencephalography Signal processing Higher order statistics Parametric statistics Data mining Spectral analysis Brain modeling Couplings Biomedical measurements |
| Content Type | Text |
| Resource Type | Article |
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