摘要
针对脑电信号的非平稳性和非线性,采用少次相干平均结合样本熵的方法对视听诱发脑电信号进行特征提取.首先,对预处理后的脑电信号进行15次相干平均,获得视觉、听觉及视听觉诱发脑电的时域特征信号;然后,将该特征信号做为原始信号输入,构成m维矢量序列,计算相关导联在靶刺激、非靶刺激和自发脑电状态的样本熵值;最后,比较分析单一视觉、听觉和视听刺激下,不同状态脑电样本熵值,文中阐明了视听觉诱发下,大脑认知的复杂性和信息耦合性.结果显示:只进行少次相干平均即可有效提取视听刺激模式下脑电的样本熵特征量,减少了因长时间视觉刺激引起神经疲劳导致的误差.同时,靶刺激的出现可使脑电样本熵值增大,表明中枢神经系统与外周刺激发生信息耦合,导致了大脑系统复杂性的提高.该研究可以应用于神经认知科学和脑-机交互系统中.
According to the electroencephalogram（EEG）signal being non-stationary and nonlinear,the method to coherent average combined with sample entropy analysis was used to extract the audio-visual evoked EEG feature.First,do15times coherent average processing after the EEG signal pretreatment, in order to get time-domain features of visual,auditory,and visual-auditory evoked EEG signals.Then, the characteristic signal as the original input composed an m-dimensional vector,by which calculating sample entropy of the related leads at the target stimuli,non-target stimuli and spontaneous EEG state. Finally,the brain cognitive complexity and information coupling under the visual and auditory evoked states can be clarified by comparing sample entropy values of three different states under the visual stimulus,auditory stimulus and visual-auditory stimulus.The results showed that this method can effectively extract the sample entropy of EEG signal under the different stimulus models by fewer times coherence average,and reduce the error caused by nerve fatigue with long time visual stimulation.At the same time,the sample entropy values increase when target stimuli show.It indicated that central nervous system can couple with peripheral stimulation,and the information coupling results in the brain complexity increased.This research can be applied to neural cognitive science and brain-computer interaction system.
出处
《测试技术学报》
2014年第3期203-208,共6页
Journal of Test and Measurement Technology
基金
国家基础科学人才培养基金资助项目(No.J1103210)
山西省自然科学基金资助项目(No.2013011016-2)
关键词
诱发脑电
相干平均
样本熵
特征提取
大脑复杂度
evoked EEG
coherent average
sample entropy
feature extraction
brain complexity
