Research map / Label-efficient and robust learning / Domain adaptation and generalization
VBH-GNN: Variational Bayesian Heterogeneous Graph Neural Networks for Cross-subject Emotion Recognition
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Proposes VBH-GNN, a variational Bayesian heterogeneous graph neural network for cross-subject EEG emotion recognition that addresses limitations of single-modal domain adaptation methods.
Problem
Cross-subject emotion recognition from EEG is hard because domain-invariant emotion features are difficult to find; existing domain adaptation methods use only single-modal EEG data.
Approach
Combines multimodal (EEG plus other physiological signals) heterogeneous graph modeling with variational Bayesian inference to learn domain-invariant emotion representations across subjects; the abstract is truncated so the exact mechanism is not fully stated.
Limitations
- Full text unavailable; only the truncated abstract was provided
Experimental setup
Builds on: domain adaptation
Closest papers by meaning
- Multi-dataset Joint Pre-training of Emotional EEG Enables Generalizable Affective Computing (NeurIPS 2025 · similarity 0.89)
- DMMR: Cross-Subject Domain Generalization for EEG-Based Emotion Recognition via Denoising Mixed Mutual Reconstruction (AAAI 2024 · similarity 0.88)
- Beyond Mimicking Under-Represented Emotions: Deep Data Augmentation with Emotional Subspace Constraints for EEG-Based Emotion Recognition (AAAI 2024 · similarity 0.86)
- FEEL: Quantifying Heterogeneity in Physiological Signals for Generalizable Emotion Recognition (NeurIPS 2025 · similarity 0.86)
- CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding (NeurIPS 2025 · similarity 0.85)
- EEVR: A Dataset of Paired Physiological Signals and Textual Descriptions for Joint Emotion Representation Learning (NeurIPS 2024 · similarity 0.84)
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