The individual difference between subjects is significant in EEG-based emotion recognition, resulting in the difficulty of sharing the model across subjects. Previous studies use domain adaptation algorithms to minimize the global domain discrepancy while ignoring the class information, which may cause misalignment of subdomains and reduce model performance. This paper proposes a multi-subdomain adversarial network (MSAN) for cross-subject EEG-based emotion recognition. MSAN uses adversarial training to model the discrepancy in the global domain and subdomain to reduce the intra-class distance and enlarge the inter-class distance. In addition, MSAN initializes parameters through a pre-trained autoencoder to ensure the stability and convertibility of the model. The experimental results show that the accuracy of MSAN is improved by 30.02\% on the SEED dataset comparing with the nontransfer method.

翻译：在基于脑电的情绪识别中，被试间的个体差异显著，导致模型难以跨被试共享。以往研究采用域适应算法最小化全局域差异，却忽略了类别信息，这可能引起子域对齐错误并降低模型性能。本文提出一种用于跨被试脑电情绪识别的多子域对抗网络（MSAN）。MSAN通过对抗训练对全局域和子域的差异进行建模，以减少类内距离并增大类间距离。此外，MSAN通过预训练的自编码器初始化参数，以确保模型的稳定性和可迁移性。实验结果表明，与无迁移方法相比，MSAN在SEED数据集上的准确率提升了30.02%。