Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 18; no. 5; pp. 1710 - 1721
• Main Authors: Liu, Shuaiqi, Wang, Xu, Zhao, Ling, Zhao, Jie, Xin, Qi, Wang, Shui-Hua
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective computing; Algorithms; Artificial intelligence; Artificial neural networks; Brain; Brain - physiology; Brain modeling; Classification algorithms; Convolutional neural network; DDE; dynamic differential entropy; EEG; Electrodes; Electroencephalography; Emotion recognition; Emotions; Emotions - classification; empirical mode decomposition; Entropy; Feature extraction; Female; Frequency-domain analysis; Heuristic algorithms; Humans; Male; Neural networks; Neural Networks, Computer; Nitrous oxide; Signal Processing, Computer-Assisted; subject-independent emotion recognition
• ISSN: 1545-5963; 1557-9964; 1557-9964
• DOI: 10.1109/TCBB.2020.3018137

Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53 percent higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.