EEG Nonlinear Feature Detection in Brain-Computation Interface

摘要：

Brain-computer interface research focused on using electroencephalogram(EEG) from the scalp over sensorimotor cortex to control outer device. The studies seek to improve the classification accuracy by improving the selection of signal features based on non-linear methods. Since EEG signals may be considered chaotic, chaos theory may supply effective quantitative descriptors of EEG dynamics and of underlying chaos in the brain. The complexity of the chaotic system can be characterized by complexity measure computed from the signals generated by the system.Two new features of EEG, Kolmogorov and C0 complexity measure are presented for analyzing EEG signals in BCI system. The experiments proved that the method is effective; the accuracy of the system reaches 90.3％.

关键词： EEG complezity measure brain-computation interface

作者: Yi Li Yingle Fan Cheng Qian

作者单位: Institute for Biomedical Engineering and Instrument Hangzhou Dianzi University Hangzhou,Zhejiang Province,China

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）