In this paper, we focus on the challenge of individual variability in affective brain-computer interfaces (aBCI), which employs electroencephalogram (EEG) signals to monitor and recognize human emotional states, thereby facilitating the advancement of emotion-aware technologies. The variability in EEG data across individuals poses a significant barrier to the development of effective and widely applicable aBCI models. To tackle this issue, we propose a novel transfer learning framework called Semi-supervised Domain Adaptation with Dynamic Distribution Alignment (SDA-DDA). This approach aligns the marginal and conditional probability distribution of source and target domains using maximum mean discrepancy (MMD) and conditional maximum mean discrepancy (CMMD). We introduce a dynamic distribution alignment mechanism to adjust differences throughout training and enhance adaptation. Additionally, a pseudo-label confidence filtering module is integrated into the semi-supervised process to refine pseudo-label generation and improve the estimation of conditional distributions. Extensive experiments on EEG benchmark databases (SEED, SEED-IV and DEAP) validate the robustness and effectiveness of SDA-DDA. The results demonstrate its superiority over existing methods in emotion recognition across various scenarios, including cross-subject and cross-session conditions. This advancement enhances the generalization and accuracy of emotion recognition, potentially fostering the development of personalized aBCI applications. The source code is accessible at https://github.com/XuanSuTrum/SDA-DDA.

翻译：本文聚焦于情感脑机接口（aBCI）中的个体差异性挑战。aBCI利用脑电图（EEG）信号监测和识别人体情感状态，从而推动情感感知技术的发展。个体间EEG数据的显著差异是构建有效且广泛适用的aBCI模型的主要障碍。为解决此问题，我们提出了一种名为“基于动态分布对齐的半监督域自适应”（SDA-DDA）的新型迁移学习框架。该方法利用最大均值差异（MMD）和条件最大均值差异（CMMD）对齐源域与目标域的边缘概率分布和条件概率分布。我们引入了动态分布对齐机制，以在训练过程中自适应调整分布差异并增强域适应能力。此外，在半监督学习过程中集成了伪标签置信度过滤模块，以优化伪标签生成并提升条件分布估计的准确性。在EEG基准数据库（SEED、SEED-IV和DEAP）上进行的大量实验验证了SDA-DDA的鲁棒性和有效性。结果表明，在包括跨被试和跨会话在内的多种场景下，该方法在情感识别任务中均优于现有方法。这一进展提升了情感识别的泛化能力和准确性，有望推动个性化aBCI应用的发展。源代码公开于：https://github.com/XuanSuTrum/SDA-DDA。