Although deep learning-based algorithms have demonstrated excellent performance in automated emotion recognition via electroencephalogram (EEG) signals, variations across brain signal patterns of individuals can diminish the model's effectiveness when applied across different subjects. While transfer learning techniques have exhibited promising outcomes, they still encounter challenges related to inadequate feature representations and may overlook the fact that source subjects themselves can possess distinct characteristics. In this work, we propose a multi-source domain adaptation approach with a transformer-based feature generator (MSDA-TF) designed to leverage information from multiple sources. The proposed feature generator retains convolutional layers to capture shallow spatial, temporal, and spectral EEG data representations, while self-attention mechanisms extract global dependencies within these features. During the adaptation process, we group the source subjects based on correlation values and aim to align the moments of the target subject with each source as well as within the sources. MSDA-TF is validated on the SEED dataset and is shown to yield promising results.

翻译：尽管基于深度学习的算法在通过脑电图（EEG）信号进行自动情绪识别方面展现出了卓越性能，但不同个体大脑信号模式的差异会降低模型在跨被试应用时的有效性。虽然迁移学习技术已取得一定成果，但仍面临特征表征不足的挑战，且可能忽视源域被试本身具有不同特征这一事实。本文提出了一种基于Transformer特征生成器的多源域自适应方法（MSDA-TF），旨在利用多个源域的信息。所提出的特征生成器保留卷积层以捕获浅层的时空频EEG数据表征，同时通过自注意力机制提取这些特征中的全局依赖关系。在自适应过程中，我们根据相关性值对源域被试进行分组，并旨在对齐目标域被试与每个源域以及源域之间的统计矩。MSDA-TF在SEED数据集上进行了验证，并取得了有前景的结果。