An EEG-Based Brain Computer Interface for Emotion Recognition and Its Application in Patients with Disorder of Consciousness

• Published in: IEEE transactions on affective computing Vol. 12; no. 4; pp. 832 - 842
• Main Authors: Huang, Haiyun, Xie, Qiuyou, Pan, Jiahui, He, Yanbin, Wen, Zhenfu, Yu, Ronghao, Li, Yuanqing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; affective computing; brain-computer interface (BCI); Brain-computer interfaces; Consciousness; disorder of consciousness (DOC); EEG; Electroencephalography; Electromagnetic compatibility; Emotion recognition; Emotional factors; Emotions; Human-computer interface; Real-time systems; Streaming media; Support vector machines
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2019.2901456

Recognizing human emotions based on electroencephalogram (EEG) signals has received a great deal of attentions. Most of the existing studies focused on offline analysis, and real-time emotion recognition using a brain computer interface (BCI) approach remains to be further investigated. In this paper, we proposed an EEG-based BCI system for emotion recognition. Specifically, two classes of video clips that represented positive and negative emotions were presented to the subjects one by one, while the EEG data were collected and processed simultaneously, and instant feedback was provided after each clip. Ten healthy subjects participated in the experiment and achieved a high average online accuracy of 91.5 <inline-formula><tex-math notation="LaTeX">\pm</tex-math> <mml:math><mml:mo>±</mml:mo></mml:math><inline-graphic xlink:href="li-ieq1-2901456.gif"/> </inline-formula> 6.34 percent. The experimental results demonstrated that the subjects emotions had been sufficiently evoked and efficiently recognized by our system. Clinically, patients with disorder of consciousness (DOC), such as coma, vegetative state, minimally conscious state and emergence minimally conscious state, suffer from motor impairment and generally cannot provide adequate emotion expressions. Consequently, doctors have difficulty in detecting the emotional states of these patients. Therefore, we applied our emotion recognition BCI system to patients with DOC. Eight DOC patients participated in our experiment, and three of them achieved significant online accuracy. The experimental results show that the proposed BCI system could be a promising tool to detect the emotional states of patients with DOC.