Multi-Scale Hyperbolic Contrastive Learning for Cross-Subject EEG Emotion Recognition

Electroencephalography (EEG) serves as a reliable and objective signal for affective computing applications. However, individual differences in EEG signals pose a significant challenge for emotion recognition tasks across subjects. To address this, we proposed a novel method called Multi-Scale Hyper...

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Bibliographic Details
Published inIEEE transactions on affective computing pp. 1 - 16
Main Authors Chang, Jiang, Zhang, Zhixin, Qian, Yuhua, Lin, Pan
Format Journal Article
LanguageEnglish
Published IEEE 2025
Subjects
Accuracy
Affective computing
Brain modeling
Computational modeling
Contrastive learning
cross-subject
Data models
EEG
Electroencephalography
Emotion recognition
Feature extraction
hyperbolic embedding
Vectors
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Summary:Electroencephalography (EEG) serves as a reliable and objective signal for affective computing applications. However, individual differences in EEG signals pose a significant challenge for emotion recognition tasks across subjects. To address this, we proposed a novel method called Multi-Scale Hyperbolic Contrastive Learning (MSHCL), which leverages event-relatedness to learn subject-invariant representations. MSHCL employs contrastive losses at two different scales-emotion and stimulus-to effectively capture complex EEG patterns within a hyperbolic space hierarchy. Our method is evaluated on three datasets: SEED, MPED, and FACED. It achieves 89.3% accuracy on the three-class task for SEED, 38.8% on the seven-class task for MPED, and 77.0% and 45.7% on the binary and nine-class tasks for FACED in cross-subject emotion recognition. These results demonstrate that the proposed MSHCL method superior performance over other baselines and its effectiveness in learning subject-invariant representations. The source code is available at https://github.com/JiangChang-BRAIN/MSHCL .
ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2025.3535542