A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition

• Published in: IEEE transactions on affective computing Vol. 12; no. 2; pp. 494 - 504
• Main Authors: Li, Yang, Zheng, Wenming, Zong, Yuan, Cui, Zhen, Zhang, Tong, Zhou, Xiaoyan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adversarial network; Biological neural networks; Brain modeling; cerebral hemisphere asymmetry; Data models; Discriminators; Domains; EEG emotion recognition; Electroencephalography; Emotion recognition; Emotional factors; Emotions; Feature extraction; Generative adversarial networks; long short term memory (LSTM); Neural networks; Neuroscience; Performance evaluation
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2018.2885474

In this paper, we propose a novel neural network model, called bi-hemisphere domain adversarial neural network (BiDANN) model, for electroencephalograph (EEG) emotion recognition. The BiDANN model is inspired by the neuroscience findings that the left and right hemispheres of human's brain are asymmetric to the emotional response. It contains a global and two local domain discriminators that work adversarially with a classifier to learn discriminative emotional features for each hemisphere. At the same time, it tries to reduce the possible domain differences in each hemisphere between the source and target domains so as to improve the generality of the recognition model. In addition, we also propose an improved version of BiDANN, denoted by BiDANN-S, for subject-independent EEG emotion recognition problem by lowering the influences of the personal information of subjects to the EEG emotion recognition. Extensive experiments on the SEED database are conducted to evaluate the performance of both BiDANN and BiDANN-S. The experimental results have shown that the proposed BiDANN and BiDANN models achieve state-of-the-art performance in the EEG emotion recognition.