摘要
针对情感识别进行研究,提出基于主成分分析法(PCA)过滤小波变换结合自回归模型提取的信号特征方法,并基于梯度提升分类树以实现情感分类.将特征提取的重点放在脑电信号变化情况以及小波分量变化情况作为脑电信号特征.采用Koelstra等提出的分析人类情绪状态的多模态标准数据库DEAP,提取8种正负情绪代表各个脑区的14个通道脑电数据.结果表明,算法对8种情感两两分类识别平均准确率为95.76%,最高准确率为98.75%,可为情感识别提供帮助.
The research is carried out for the purpose of emotion recognition, and the signal feature method of wavelet filtering transformation combined with autoregressive model extraction is proposed on the basis of Principal Component Analysis (PCA). Besides, sentiment classification is realized on the basis of gradient promotion classification tree. The focus of feature extraction is laid on the changes of Electro Encephalo Gram (EEG) signals and the changes of wavelet components as features of EEG signals. The multimodal standard database DEAP proposed by Koelstra et al. to analyze human emotional state is adopted to extract eight positive and negative emotions to represent 14 channels of EEG data in each brain region. The results suggest that the average accuracy of the algorithm for 8 kinds of emotions in pairwise classification is 95.76%, and the highest accuracy is 98.75%, making it possible to help emotional recognition.
作者
刘一
谢懿
LIU Yi;XIE Yi(School of Electronics and Information,Guangdong Polytechnic Normal University,Guangzhou 510665,China)
出处
《计算机系统应用》
2019年第5期119-124,共6页
Computer Systems & Applications
基金
2018年度国家级大学生创新创业训练计划(201810588006)~~
关键词
自回归
小波变换
主成分分析
情感评估
auto-regressive
wavelet transform
principal component analysis
emotion assessment
