Abstract
Emotion recognition based on electroencephalography (EEG) signals is increasingly becoming a key research hotspot in affective Brain-Computer Interfaces (aBCIs). However, the current transfer learning model greatly depends on the source domain and target domain data, which hinder the practical application of emotion recognition. Therefore, we propose a Multi-domain Aggregation Transfer Learning framework for EEG emotion recognition with Domain-Class prototype under unseen targets (MATL-DC). We design the feature decoupling module to decouple class-invariant domain features from domain-invariant class features from shallow features. In the model training stage, the multi-domain aggregation mechanism aggregates the domain feature space to form a superdomain, which enhances the characteristics of emotional EEG signals. In each superdomain, we further extract the class prototype representation by class features. In addition, we adopt the pairwise learning strategy to transform the sample classification problem into the similarity problem between sample pairs, which effectively alleviates the influence of label noise. It is worth noting that the target domain is completely unseen during the training process. In the inference stage, we use the trained domain-class prototypes for inference, and then realize emotion recognition. We rigorously validate it on the publicly available databases (SEED, SEED-IV and SEED-V). The results show that the accuracy of MATL-DC model is 84.70\%, 68.11\% and 61.08\%, respectively. MATL-DC achieves comparable or even better performance than methods that rely on both source and target domains. The source code is available at this https URL.
Abstract (translated)
基于脑电图（EEG）信号的情感识别在情感型脑机接口（aBCIs）中正逐渐成为关键的研究热点。然而，目前的迁移学习模型严重依赖于源域和目标域的数据，这阻碍了情感识别的实际应用。因此，我们提出了一种基于未知目标领域下的多域聚合迁移学习框架（MATL-DC），用于EEG情绪识别，并采用领域-类别原型设计。我们设计了一个特征解耦模块，从浅层特征中分离出类不变领域的特征和领域不变的类别特征。在模型训练阶段，多域聚合机制将各个领域的特征空间聚集在一起形成一个超域，从而增强了情感EEG信号的特点。每个超域中，进一步通过类别特征提取类别原型表示。此外，我们采用了成对学习策略，将样本分类问题转化为样本对之间的相似性问题，有效缓解了标签噪声的影响。值得注意的是，在训练过程中目标领域是完全未见的。在推理阶段，我们使用训练好的领域-类别原型进行推理，然后实现情绪识别。 我们在公开可用的数据集（SEED、SEED-IV和SEED-V）上对其进行了严格的验证。结果表明，MATL-DC模型的准确率分别为84.70%、68.11%和61.08%，相较于依赖于源域与目标域的方法，MATL-DC取得了相当或更好的性能表现。 该研究的源代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2509.01135
