Multi-Channel EEG Based Emotion Recognition Using Temporal Convolutional Network and Broad Learning System

• Published in: Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 2452 - 2457
• Main Authors: Jia, Xue, Zhang, Tong, Philip Chen, C. L., Liu, Zhulin, Chen, Long, Wen, Guihua, Hu, Bin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.10.2020
• Subjects: Brain modeling; broad learning system (BLS); Convolution; EEG; Electroencephalography; Emotion recognition; Feature extraction; Learning systems; Task analysis; temporal convolutional broad learning system (TCBLS); temporal convolutional network (TCN)
• ISSN: 2577-1655
• DOI: 10.1109/SMC42975.2020.9283159

Automatic real-time emotion recognition based on multi-channel EEG signals is a significant and challenging task in neurology and psychiatry. In recent years, deep learning has been used in EEG emotion recognition. However, many existing deep learning based methods still require complex pre-processing or additional feature extraction, which make it difficult to achieve real-time emotion recognition. In this paper, an end-to-end model named Temporal Convolutional Broad Learning System (TCBLS) was designed for multi-channel EEG based emotion recognition. The TCBLS takes one-dimensional EEG signals as input, then extracts emotion-related features of EEG automatically. In this model, the Temporal Convolutional Network (TCN) is designed to extract EEG temporal features and deep abstract features simultaneously, then Broad Learning System (BLS) is used to map the features to a more discriminative space and further enhance the features. We evaluated our method on DEAP database, performing 10-fold cross-validation on each subject to obtain the classification accuracy. Experimental results indicate that the performance of TCBLS is better than other comparison methods, and the mean accuracy of TCBLS is 99.5755% and 99.5781% on valence and arousal classification task respectively. The results demonstrate the effectiveness and robustness of TCBLS in EEG emotion recognition.