With the advancement of science and technology, the importance of emotion research has become increasingly evident. Electroencephalography (EEG)-based emotion recognition has emerged as an active research area in recent years, owing to its objectivity and high temporal resolution. However, most existing methods focus on optimizing encoder structures to enhance feature extraction capabilities, while paying relatively little attention to similarity calculation strategies, particularly overlooking the potential temporal misalignment of responses among different subjects. To address these shortcomings, this paper draws inspiration from the late interaction mechanism of ColBERT in natural language processing (NLP) and proposes a Temporal Asynchronous Alignment-based Contrastive Learning (TA2CL) framework. This method transforms the traditional global "hard alignment" similarity calculation approach into a fine-grained local matching mechanism, enabling the model to adaptively search for and align "locally highly correlated" segments between two EEG signals, thereby effectively mitigating the effects of inter-subject differences and temporal delays. Experimental results demonstrate that the proposed method achieves strong performance across multiple public datasets. Specifically, on the FACED dataset, it achieves an accuracy of 64.5% for the nine-class classification task and 79.5% for the binary classification task, while on the SEED and SEED-V datasets, it achieves accuracies of 86.4% and 70.1%, respectively, validating the method's effectiveness and generalization capability.

翻译：随着科技的进步，情感研究的重要性日益凸显。基于脑电信号的情感识别因其客观性与高时间分辨率，近年来成为活跃的研究领域。然而，现有方法大多聚焦于优化编码器结构以增强特征提取能力，而对相似度计算策略关注相对较少，尤其忽略了不同被试响应间潜在的时间错位问题。针对上述不足，本文借鉴自然语言处理中ColBERT模型的延迟交互机制，提出了一种基于时序异步对齐的对比学习框架。该方法将传统的全局“硬对齐”相似度计算方式转化为细粒度的局部匹配机制，使模型能够自适应搜索并对齐两段脑电信号间的“局部高相关”片段，从而有效缓解被试间差异与时间延迟造成的影响。实验结果表明，所提方法在多个公开数据集上表现优异。具体地，在FACED数据集上，九分类任务准确率达64.5%，二分类任务达79.5%；在SEED和SEED-V数据集上，准确率分别为86.4%和70.1%，验证了方法的有效性与泛化能力。