Research on Visual Attention Classification Based on EEG Entropy Parameters

摘要：

　　In this study,two electroencephalogram (EEG) feedback experiments were completed to measure the different levels of visual attention.In order to assess different visual attention levels,EEG data were processed with nonlinear dynamics parameters based on sequence complexity which involves approximate entropy,sample entropy and multi-scale entropy.According to the statistical analysis of 14 subjects’ EEG signal by using entropy as parameters,significant differences in attention intensity have been found in most of the electrodes in frontal regions and some of the electrodes in temporal regions.The values of entropy indicate a declining tendency with the decreasing level of attention and among all the parameters,sample entropy achieves the highest sensitivity in the classification performance of visual attention.We also applied a classifier based on support vector machine (SVM) to discriminate the different levels of attention which finally achieved a reasonable recognition ratio of 85.24%.

关键词： visual attention classification entropy support vector machine

作者: Wen Li Dong Ming Rui Xu Hao Ding Hongzhi Qi Baikun Wan

作者单位: Department of Biomedical Engineering, Tianjin University, Tianjin 300072, P. R. China

会议类型: 国际会议

会议名称: World Congress on Medical Physics and Biomedical Engineering (2012年医学物理及生物医学工程国际会议(IFMBE))

会议地点: 北京

会议语种:英文

页码: 1553-1556

在线出版日期: 2012-05-26（万方平台首次上网日期，不代表论文的发表时间）