Electroencephalogram (EEG) emotion recognition plays an important role in human–computer interaction, and a higher recognition accuracy can improve the user experience. In recent years, domain adaptive methods in transfer learning have been used to construct a general emotion recognition model to deal with domain difference among different subjects and sessions. However, it is still challenging to effectively reduce domain difference in domain adaptation. In this paper, we propose a Multiple-Source Distribution Deep Adaptive Feature Norm Network for EEG emotion recognition, which reduce domain difference by improving the transferability of task-specific features. In detail, the domain adaptive method of our model employs a three-layer network topology, inserts Adaptive Feature Norm to self-supervised adjustment between different layers, and combines a multiple-kernel selection approach to mean embedding matching. The method proposed in this paper achieves the best classification performance in the SEED and SEED-IV datasets. In SEED dataset, the average accuracy of cross-subject and cross-session experiments is 85.01 and 91.93%, respectively. In SEED-IV dataset, the average accuracy is 58.81% in cross-subject experiments and 59.51% in cross-session experiments. The experimental results demonstrate that our method can effectively reduce the domain difference and improve the emotion recognition accuracy.

脑电图（EEG）情绪识别在人机交互中发挥着重要作用，较高的识别准确率可以改善用户体验。近年来，迁移学习中的领域自适应方法已被用来构建通用情感识别模型，以处理不同主题和会话之间的领域差异。然而，有效减少领域适应中的领域差异仍然具有挑战性。在本文中，我们提出了一种用于脑电图情感识别的多源分布深度自适应特征规范网络，通过提高任务特定特征的可迁移性来减少域差异。具体来说，我们模型的域自适应方法采用三层网络拓扑，插入自适应特征范数以在不同层之间进行自监督调整，并结合多核选择方法进行均值嵌入匹配。本文提出的方法在 SEED 和 SEED-IV 数据集中实现了最佳分类性能。在 SEED 数据集中，跨主题和跨会话实验的平均准确率分别为 85.01 和 91.93%。在 SEED-IV 数据集中，跨主题实验的平均准确率为 58.81%，跨会话实验的平均准确率为 59.51%。实验结果表明，我们的方法可以有效地减少域差异，提高情感识别的准确性。