Cross-modal synchronization of EEG and ECG reveals hidden signatures of recovery in traumatic brain injury.

Accurate assessment of traumatic brain injury (TBI) is critical for customization of neurorehabilitation treatments and clinical decision-making. Existing monitoring approaches either rely on subjective evaluation or isolated physiological signals, limiting real-time responsiveness and multimodal insight. This study introduces a novel framework integrating electroencephalography (EEG) and electrocardiography (ECG) to explore heart-brain synchronization as a biomarker for neurological state in TBI patients. We first define a synchronization metric using EEG delta power and heart rate variability (HRV), capturing both the degree and direction of synchronization. A two-stage contrastive learning approach is then proposed: Clinically Consistent Contrastive Learning (CCCL) leverages clinical metrics to guide positive sample selection, while Multimodal Heart-brain Contrastive Learning (MHCL) aligns synchronization features with clinical outcomes. Applied to long-term ICU recordings, the proposed approach identifies distinct synchronization patterns associated with recovery trajectories. Although the sample size (N=11) is expected to be extended, this work offers an exploratory, proof-of-concept demonstration of heart-brain synchronization as a potential real-time biomarker for neurophysiological recovery in severe TBI.