Recognizing the pivotal role of EEG emotion recognition in the development of affective Brain-Computer Interfaces (aBCIs), considerable research efforts have been dedicated to this field. While prior methods have demonstrated success in intra-subject EEG emotion recognition, a critical challenge persists in addressing the style mismatch between EEG signals from the source domain (training data) and the target domain (test data). To tackle the significant inter-domain differences in cross-dataset EEG emotion recognition, this paper introduces an innovative solution known as the Emotional EEG Style Transfer Network (E$^2$STN). The primary objective of this network is to effectively capture content information from the source domain and the style characteristics from the target domain, enabling the reconstruction of stylized EEG emotion representations. These representations prove highly beneficial in enhancing cross-dataset discriminative prediction. Concretely, E$^2$STN consists of three key modules\textemdash transfer module, transfer evaluation module, and discriminative prediction module\textemdash which address the domain style transfer, transfer quality evaluation, and discriminative prediction, respectively. Extensive experiments demonstrate that E$^2$STN achieves state-of-the-art performance in cross-dataset EEG emotion recognition tasks.

翻译：摘要：脑电情绪识别在情感脑机接口（aBCIs）发展中具有关键作用，因此该领域已投入大量研究工作。尽管现有方法在个体内脑电情绪识别上取得了成功，但源域（训练数据）与目标域（测试数据）脑电信号之间的风格失配问题仍是亟待解决的挑战。为应对跨数据集脑电情绪识别中显著的域间差异，本文提出一种创新解决方案——情绪脑电风格迁移网络（E$^2$STN）。该网络的核心目标是有效捕获源域的内容信息与目标域的风格特征，从而重构具有风格化特征的脑电情绪表征。这些表征在提升跨数据集判别预测能力方面具有显著优势。具体而言，E$^2$STN包含三个关键模块——迁移模块、迁移评估模块和判别预测模块，分别负责域风格迁移、迁移质量评估与判别预测。大量实验证明，E$^2$STN在跨数据集脑电情绪识别任务中达到了当前最优性能。