ArticleScientific reports2025
Cross-subject EEG signals-based emotion recognition using contrastive learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Adaptive frequency band attention-guided CNN-BiLSTM for Spatial-Spectral-Temporal EEG emotion recognition.Brain informatics · 2026Article
- [An electroencephalogram-based emotion recognition method using multi-branch convolutional neural networks and Transformer].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Adaptive graph convolution domain adaptation network for cross-subject EEG emotion recognition.Medical & biological engineering & computing · 2026Article
- [A group-level stimulus-aware self-supervised soft contrastive learning framework for electroencephalogram emotion recognition].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Deception Detection from Five-Channel Wearable EEG on LieWaves: A Reproducible Baseline for Subject-Dependent and Subject-Independent Evaluation.Sensors (Basel, Switzerland) · 2026Article
- A multi-task EEG emotion recognition method based on emotion-dimension coupling constraints.Scientific reports · 2026Article
- Cross-subject generalization for EEG emotion recognition: a review of methods, challenges, and future trends.Frontiers in computational neuroscience · 2026Review
- Unsupervised clustering of pre-ictal EEG in children: a reproducible and lightweight CPU-based workflow.Frontiers in neurology · 2025Article
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6 authors.
Funding
Abstract
Electroencephalography (EEG) signals based emotion brain computer interface (BCI) is a significant field in the domain of affective computing where EEG signals are the cause of reliable and objective applications. Despite these advancements, significant challenges persist, including individual differences in EEG signals across subjects during emotion recognition. To cope this challenge, current study introduces a cutting-edge cross subject contrastive learning (CSCL) scheme for EEG signals representation of brain region. The proposed scheme addresses the generalisation across subjects directly, which is a primary challenge in EEG signals-based emotions recognition. The proposed CSCL scheme captures the complex patterns effectively by employing emotions and stimulus contrastive losses within hyperbolic space. CSCL is designed primarily to learn representations that can effectively distinguish signals originating from different brain regions. Further, we evaluate the significance of our proposed CSCL scheme on five different datasets, including SEED, CEED, FACED and MPED, and obtain 97.70%, 96.26%, 65.98%, and 51.30% respectively. The experimental results show that our proposed CSCL scheme demonstrates strong effectiveness while addressing the challenges related to cross subject variability and label noise in the EEG-based emotion recognition system.
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