Wearable Wireless Dual Channel EEG System for Emotion Recognition Based on Machine Learning

• Published in: IEEE sensors journal Vol. 23; no. 18; p. 1
• Main Authors: Wang, Yue, Tian, Wei, Xu, Jingyi, Tian, Yingnan, Xu, Chengtao, Ma, Biao, Hao, Qing, Zhao, Chao, Liu, Hong
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Channels; EEG; Electrodes; Electroencephalography; Emotion recognition; Emotions; Feature extraction; Machine learning; Optimization; Sensors; wearable device; Wearable technology; Wireless communication; Wireless sensor networks
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3303441

Objective : Emotion recognition is critical for promoting mental health, as too much negative emotions may cause mental illness, especially in the era of COVID-19. EEG is the dominating modality to study brain dynamics. However, most of current EEG devices were designed for as much applications as possible with unnecessary electrodes for emotion recognition applications. Methods : In this paper, a wearable and wireless EEG device with only two channels were specifically designed for emotion recognition. The device is minimized and could be embedded in a headband. Novel preprocessing algorithm to remove ocular artifacts, features selection and optimization, comparison between four machine learning methods were studied to demonstrate a high classification accuracy of emotion valence on 20 subjects. Conclusions : As our wearable EEG system achieved high accuracy with only two channels, it would broaden the application perspective of emotion recognition, and could be applied in outdoor environment or other scenarios.