HB-IGCL: Heart-Brain Instance-Global Contrastive Learning for Emotion Recognition using Long-duration ECG and EEG

Emotion recognition is a key topic in affective computing, where electrocardiogram (ECG) and electroencephalogram (EEG) provide complementary peripheral and central physiological views. For long-duration recordings, a common practice is to divide each recording into short segments and train segment representations with the recording-level emotion label. This practice is convenient but conceptually restrictive: it assumes instance-level emotion supervision that is not actually observed, while emotional physiological responses may vary substantially across time. In addition, many ECG-EEG methods process the two modalities independently or combine them only through late fusion, underusing the natural correspondence between synchronized cardiac and neural observations. To address these issues, we propose Heart-Brain Instance-Global Contrastive Learning (HB-IGCL). Rather than treating every segment as an emotion-labeled sample, HB-IGCL learns whether a local physiological instance is compatible with the paired global context from the other modality within the same synchronized recording. ECG-to-EEG and EEG-to-ECG objectives provide bidirectional local-to-global contextual alignment, and a spatial-guided alignment module (SGAM) further relates lead-level spatial structure to global temporal context. We evaluate HB-IGCL in subject-independent experiments on AMIGOS, DREAMER, and MPED. Results show that the proposed framework improves representation learning across multiple encoders and datasets.