| 英文标题 | Fast sample entropy electroencephalogram emotion analysis of double sliding coarse granulation |
| 作者英文名 | Zhu Yongsheng, Zhong Qinghua, Cai Dongli, Liao Jinxiang |
| 机构英文名 | School of Physics & Telecommunication Engineering,South China Normal University,Guangzhou 510006,China |
| 英文摘要 | Aiming at the problems of traditional single-scale sample entropy that couldn't be obvious to extract electroencephalogram(EEG) sequence features, multi-scale entropy would miss important information in the coarse granulation process that decreased the performance of emotion classification, and the efficiency of sample entropy algorithm was not high, this paper proposed a multi-scale fast sample entropy EEG feature extraction method based on double sliding mean coarse granulation. Firstly, it processed the double sliding mean coarse-grained EEG signals in multiple scales because of the difference of different emotional EEG signals. Secondly, it used the fast sample entropy algorithm to extract sample entropy values of different time scales as eigenvectors. Lastly, it used the random forest(RF) classification model to identify different emotional states. This paper studied the proposed method in DEAP, a multi-mode standard emotion database, and it was found that the frontal area of the brain and the right brain were relatively sensitive to emotions, and the positive, neutral and negative emotions achieved an average classification accuracy of 88.75% in the lateral frontal area of the brain. Experimental results show that the proposed method can effectively extract EEG features and ensure the efficiency of the algorithm. |
| 英文关键词 | EEG; emotion recognition; double sliding mean coarse granulation; FAST sample entropy; random forest |