基于小波相干算法的脑区情绪特性研究  

Research on Emotional Characteristics of Brain Regions Based on Wavelet Coherence Algorithm

作　　者：王湖斐 郭茂田[1] WANG Hufei;GUO Maotian(Institute of Physical Science and Engineering,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]郑州大学物理工程学院,郑州450001

出　　处：《传感技术学报》2020年第1期63-67,90,共6页Chinese Journal of Sensors and Actuators

摘　　要：研究了人脑不同区域情绪脑电信号的差异特性。按照国际10-20电极分布系统将大脑分成5个脑区,选用视频情绪诱发素材诱发被试产生正性、中性、负性情绪同时采集其脑电信号,设置各脑区小波相干指数为参数,研究其差异性并进行模式识别。结果显示:不同情绪状态下额叶、顶叶δ波段的小波相干指数具有显著差异(p<0.05),并且统计发现将中性情绪小波相干指数作为基准,负性情绪的小波相干指数增大,正性情绪的小波相干指数降低。实验结果验证了额叶和顶叶的小波相干指数对情绪三分类问题有较好的识别效果,顶叶情绪识别率高达96.67%,进一步证明了情绪处理时额叶、顶叶两个脑区被激活,且不同情绪状态下激活程度不同。The diversities of emotional EEG signals in different human brain regions are studied.According to the international 10-20 electrode distribution system,the brain is divided into five brain regions.The subjects were induced to generate positive,neutral,and negative emotions,and their EEG signals were collected at the same time.This paper induced the subjects’positive,neutral,and negative emotions and collected the corresponding electroencephalogram emotional signals.The wavelet coherence index of brain region was extracted as the parameter for pattern recognition.The results show that the wavelet coherence indices of frontal lobe and parietal’δ frequency band are significantly different in different emotional states(p<0.05).Considering the neutral emotion,the wavelet coherence indices of negative emotions are increased and the wavelet coherence indices of positive emotions are decreased.And the classifier based on wavelet coherence index has a good recognition effect on the emotional three classification problem.The parietal emotion rate is as high as 96.67%.

关 键 词：脑电信号 情绪脑区 小波相干算法 支持向量机 频带特征 

分 类 号：R318.04[医药卫生—生物医学工程]