title: Emotion recognition based on the sample entropy of EEG.
abstract: A sample entropy (SampEn)-based emotion recognition approach was presented. The SampEn results of notable EEG channels screened by K-S test were fed to the support vector machine (SVM)-weight classifier for training, after which it was applied to two emotion recognition tasks. One is to distinguish positive and negative emotion with high arousal and the other genitive emotion with different arousal status. Results showed that channels related to emotions were mostly located on the prefrontal region, i.e., F3, CP5, FP2, FZ, and FC2. And they were applied to form the input vectors of SVM-weight classifier. The accuracies of the present algorithm for the two tasks were 80.43% and 79.11%, respectively indicated by the leave-one-person-out validation procedure, demonstrating that the present algorithm had a reasonable generalization capability.
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China The International WIC Institute, Beijing University of Technology, Beijing 100022, People's Republic of China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Jie
authors.forename: Rui
authors.initials: R
authors.lastname: Cao
authors.forename: Li
authors.initials: L
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001143:Arousal; D016477:Artifacts; D062207:Brain-Computer Interfaces; D016208:Databases, Factual; D004569:Electroencephalography; D004644:Emotions; D019277:Entropy; D006801:Humans; D009146:Music; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
keywords: EEG; Emotion recognition; SVM; brain computer interface; sample entropy