Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces represents a significant area within the field of affective computing. In the present study, we propose a novel non-deep transfer learning method, termed as Manifold-based Domain adaptation with Dynamic Distribution (MDDD). The proposed MDDD includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The experimental results indicate that MDDD outperforms traditional non-deep learning methods, achieving an average improvement of 3.54%, and is comparable to deep learning methods. This suggests that MDDD could be a promising method for enhancing the utility and applicability of aBCIs in real-world scenarios.

翻译：基于脑电图的情感脑机接口进行情绪解码是情感计算领域的重要研究方向。本研究提出了一种新型非深度迁移学习方法——基于流形动态分布对齐的域适应方法（MDDD）。该方法包含四个核心模块：流形特征变换、动态分布对齐、分类器学习以及集成学习。数据被映射至最优格拉斯曼流形空间，从而动态对齐源域与目标域。该过程根据边际分布与条件分布的重要性对其进行优先级调整，确保针对不同类型数据的高效域适应能力。在分类器学习中，结合结构风险最小化原则构建鲁棒分类模型，并通过动态分布对齐迭代优化分类器。此外，集成学习模块聚合优化过程中不同阶段获得的分类器，利用分类器多样性提升整体预测精度。实验结果表明，MDDD优于传统非深度学习方法，平均性能提升3.54%，且与深度学习方法性能相当。这表明MDDD有望成为增强现实场景中情感脑机接口实用性与适用性的有效方法。