Relevance Vector Machine Based EEG Emotion Recognition

摘要：

　　Personal emotions accompany us in our daily life,affecting our learning and work,therefore it is necessary to obtain better understanding of human behavior through emotional assessment.This paper proposes a method for recognizing emotions electroencephalography(EEG) based on relevance vector machine(RVM).Emotional states of two types as positive and negative were selected from a standard database of DEAP,with relevance vector machine and support vector machine(SVM) to apply classification and comparison.Experimental results show that RVM classification accuracy was 93.33% and the test run time was 0.0156s; while SVM classification accuracy was 78.67% and the test run time was 0.0211s.Compared with SVM,RVMs time complexity and test error rate are lower,and its classification performance is better.

关键词： Relevance vector machine Emotion recognition EEG

作者: Li Xin Sun Xiao-Qi Qi Xiao-Ying Sun Xiao-Feng

作者单位: Institute of Biomedical Engineering Yanshan University Qinhuangdao 066004,China Electrical Engineering and Automation Yanshan University Qinhuangdao 066004,China

会议类型: 国际会议

会议名称: 2016 Sixth International Conference on Instrumentation and Measurement,Computer,Communication and Control (IMCCC2016)(第六届仪器测量、计算机通信与控制国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 293-297

在线出版日期: 2016-07-21（万方平台首次上网日期，不代表论文的发表时间）