{"ID":2844960,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05293","arxiv_id":"2511.05293","title":"Cross-domain EEG-based Emotion Recognition with Contrastive Learning","abstract":"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.","short_abstract":"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, ca...","url_abs":"https://arxiv.org/abs/2511.05293","url_pdf":"https://arxiv.org/pdf/2511.05293v2","authors":"[\"Rui Yan\",\"Yibo Li\",\"Han Ding\",\"Fei Wang\"]","published":"2025-11-07T14:55:10Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":607333,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2844960,"paper_url":"https://arxiv.org/abs/2511.05293","paper_title":"Cross-domain EEG-based Emotion Recognition with Contrastive Learning","repo_url":"https://github.com/Departure2021/EmotionCLIP","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}