传感技术学报2019，Vol.32Issue(1)：82-88,7.DOI:10.3969/j.issn.1004-1699.2019.01.015
基于脑电信号的情绪特征提取与分类
Emotional Feature Extraction and Classification Based on EEG Signals
摘要
Abstract
As an advanced function of human brain, emotion has a great impact on people's personality characteristics and mental health. By using the online Deap database, emotions are divided according to psychological valence and arousal level, and the two emotions of stress and calm are studied and analyzed. On the basis of using db4 wavelet decomposition and reconstruction algorithm to decompose the signal, according to the characteristics of the asymmetry of EEG signals in the generation of emotions, a new method of emotional feature extraction is proposed, By dividing the differential entropy of right leads by the difference between left and right symmetrical electrodes, and dividing the differential entropy of right leads by the sum of the differential entropy of left and right symmetrical electrodes, the asymmetric entropy characteristics of EEG signals is extracted. Using the support vector machine optimized by genetic algorithm for emotion classification recognition, the average recognition rate is 88.625％.Comparing with the classification recognition rate of traditional features, the classification recognition rate using the asymmetric entropy feature is significantly improved.关键词
脑电信号/情绪识别/小波分解/不对称熵/支持向量机/遗传算法Key words
EEG signal/emotion recognition/wavelet decomposition/asymmetric entropy/support vector machine/genetic algorithm分类
信息技术与安全科学引用本文复制引用
柳长源,李文强,毕晓君..基于脑电信号的情绪特征提取与分类[J].传感技术学报,2019,32(1):82-88,7.基金项目
国家自然科学基金项目(51779050) （51779050）
黑龙江省自然科学基金项目(F2016022) （F2016022）
