Electroencephalogram (EEG)-based emotion recognition is vital for affective computing but faces challenges in feature utilization and cross-domain generalization. This work introduces EmotionCLIP, which reformulates recognition as an EEG-text matching task within the CLIP framework. A tailored backbone, SST-LegoViT, captures spatial, spectral, and temporal features using multi-scale convolution and Transformer modules. Experiments on SEED and SEED-IV datasets show superior cross-subject accuracies of 88.69\% and 73.50\%, and cross-time accuracies of 88.46\% and 77.54\%, outperforming existing models. Results demonstrate the effectiveness of multimodal contrastive learning for robust EEG emotion recognition. The code is available at https://github.com/Departure2021/EmotionCLIP.

翻译：基于脑电图的情绪识别对情感计算至关重要，但在特征利用和跨域泛化方面面临挑战。本研究提出EmotionCLIP，将识别任务重新定义为CLIP框架内的脑电-文本匹配任务。通过定制化主干网络SST-LegoViT，利用多尺度卷积和Transformer模块捕捉空间、频谱和时序特征。在SEED和SEED-IV数据集上的实验显示，其跨被试准确率分别达到88.69%和73.50%，跨时段准确率分别达到88.46%和77.54%，优于现有模型。结果表明多模态对比学习对实现鲁棒的脑电情绪识别具有显著效果。代码公开于https://github.com/Departure2021/EmotionCLIP。