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.
@article{arxiv.2511.05293,
title = {Cross-domain EEG-based Emotion Recognition with Contrastive Learning},
author = {Rui Yan and Yibo Li and Han Ding and Fei Wang},
journal= {arXiv preprint arXiv:2511.05293},
year = {2026}
}