EEG emotion recognition has been hampered by the clear individual differences in the electroencephalogram (EEG). Nowadays, domain adaptation is a good way to deal with this issue because it aligns the distribution of data across subjects. However, the performance for EEG emotion recognition is limited by the existing research, which mainly focuses on the global alignment between the source domain and the target domain and ignores much fine-grained information. In this study, we propose a method called Graph-based Unsupervised Subdomain Adaptation (Gusa), which simultaneously aligns the distribution between the source and target domains in a fine-grained way from both the channel and emotion subdomains. Gusa employs three modules, such as the Node-wise Domain Constraints Module to align each EEG channel and obtain a domain-variant representation, the Class-level Distribution Constraints Module, and the Emotion-wise Domain Constraints Module, to collect more fine-grained information, create more discriminative representations for each emotion, and lessen the impact of noisy emotion labels. The studies on the SEED, SEED-IV, and MPED datasets demonstrate that Gusa significantly improves the ability of EEG to recognize emotions and can extract more granular and discriminative representations for EEG.

中文翻译：

---

Gusa：基于图的无监督子域适应跨主题脑电图情绪识别


脑电图（EEG）中明显的个体差异阻碍了脑电图情绪识别。如今，领域适应是解决这个问题的好方法，因为它可以调整跨主题的数据分布。然而，脑电图情感识别的性能受到现有研究的限制，主要关注源域和目标域之间的全局对齐，而忽略了很多细粒度信息。在本研究中，我们提出了一种称为基于图的无监督子域适应（Gusa）的方法，该方法从通道子域和情感子域以细粒度的方式同时对齐源域和目标域之间的分布。 Gusa采用三个模块，例如用于对齐每个EEG通道并获得域变量表示的节点级域约束模块、类级分布约束模块和情感级域约束模块，以收集更细粒度的数据信息，为每种情绪创建更具辨别力的表示，并减少嘈杂的情绪标签的影响。对 SEED、SEED-IV 和 MPED 数据集的研究表明，Gusa 显着提高了脑电图识别情绪的能力，并且可以为脑电图提取更精细和有辨别力的表示。