Cross-subject EEG-based Emotion Recognition Using Adversarial Domain Adaption with Attention Mechanism

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 1140 - 1144
• Main Authors: Ye, Yalan, Zhu, Xin, Li, Yunxia, Pan, Tongjie, He, Wenwen
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Adaptation models; Convolution; Design methodology; Electroencephalography; Emotion recognition; Emotions; Feature extraction; Neural networks; Neural Networks, Computer; Research Design; Reward; Training
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9630777

Cross-subject EEG-based emotion recognition (ER) is a rewarding work in real-life applications, due to individual differences between one subject and another subject. Most existing studies focus on training a subject-specific ER model. However, it is time-consuming and unrealistic to design the customized subject-specific model for a new subject in cross-subject scenarios. In this paper, we propose an Adversarial Domain Adaption with an Attention Mechanism method for EEG-based ER, namely ADAAM-ER, to decrease the individual discrepancy. ADAAM-ER consists of a Graph Convolution Neural Networks with CNNs (GCNN-CNNs) and an Adversarial Domain Adaption with a Level-wise Attention Mechanism (ADALAM). Specifically, GCNN-CNNs as a feature extractor, which constructs a broader feature space, is designed to obtain more discriminative features. And ADALAM, which can decrease the individual discrepancy by alignment of the more transferable feature regions, is introduced to further obtain the discriminative features with higher transferability. Consequently, the proposed ADAAM-ER method can design a more transferable emotion recognition model with more discriminative features for a new subject via improving transferability. Experimental results on the SEED dataset have verified the effectiveness of the proposed ADAAM-ER method with the mean accuracy of 86.58%.