As the key to realizing aBCIs, EEG emotion recognition has been widely studied by many researchers. Previous methods have performed well for intra-subject EEG emotion recognition. However, the style mismatch between source domain (training data) and target domain (test data) EEG samples caused by huge inter-domain differences is still a critical problem for EEG emotion recognition. To solve the problem of cross-dataset EEG emotion recognition, in this paper, we propose an EEG-based Emotion Style Transfer Network (E2STN) to obtain EEG representations that contain the content information of source domain and the style information of target domain, which is called stylized emotional EEG representations. The representations are helpful for cross-dataset discriminative prediction. Concretely, E2STN consists of three modules, i.e., transfer module, transfer evaluation module, and discriminative prediction module. The transfer module encodes the domain-specific information of source and target domains and then re-constructs the source domain's emotional pattern and the target domain's statistical characteristics into the new stylized EEG representations. In this process, the transfer evaluation module is adopted to constrain the generated representations that can more precisely fuse two kinds of complementary information from source and target domains and avoid distorting. Finally, the generated stylized EEG representations are fed into the discriminative prediction module for final classification. Extensive experiments show that the E2STN can achieve the state-of-the-art performance on cross-dataset EEG emotion recognition tasks.

翻译：作为实现主动式脑机接口的关键，脑电情绪识别已受到众多研究者的广泛关注。以往方法在面向个体内部的脑电情绪识别中表现良好。然而，源域（训练数据）与目标域（测试数据）脑电样本因跨域巨大差异导致的风格不匹配，仍是脑电情绪识别面临的关键问题。为解决跨数据集脑电情绪识别问题，本文提出一种基于脑电的情绪风格迁移网络（E2STN），通过获取包含源域内容信息与目标域风格信息的脑电表征（称为风格化情绪脑电表征），从而有助于跨数据集判别性预测。具体而言，E2STN由三个模块构成：迁移模块、迁移评估模块和判别预测模块。迁移模块对源域和目标域的域特定信息进行编码，随后将源域的情绪模式与目标域的统计特征重新构建为新的风格化脑电表征。在此过程中，迁移评估模块用于约束生成的表征，使其能更精确地融合来自源域与目标域的两种互补信息，并避免信息失真。最后，生成的风格化脑电表征被输入判别预测模块进行最终分类。大量实验表明，E2STN在跨数据集脑电情绪识别任务中能取得最先进的性能。