Leveraging Foundational Models and Simple Fusion for Multi-modal Physiological Signal Analysis

• URL: http://arxiv.org/abs/2512.15250v1
• Date: Wed, 17 Dec 2025 09:49:06 GMT
• Title: Leveraging Foundational Models and Simple Fusion for Multi-modal Physiological Signal Analysis
• Authors: Youssef Ghallab, Omar Iraqy, Mohamed Kandil, Mohamed Ashraf, Saadeldine Eletter, Morougue Ghazal, Ayman Khalafallah, Nagwa El-Makky,
• Abstract summary: We adapt the CBraMod encoder for large-scale self-supervised ECG pretraining.<n>We utilize a pre-trained CBraMod encoder for EEG and pre-train a symmetric ECG encoder.<n>Our approach achieves near state-of-the-art performance, demonstrating that carefully designed physiological encoders, even with straightforward fusion, substantially improve downstream performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Physiological signals such as electrocardiograms (ECG) and electroencephalograms (EEG) provide complementary insights into human health and cognition, yet multi-modal integration is challenging due to limited multi-modal labeled data, and modality-specific differences . In this work, we adapt the CBraMod encoder for large-scale self-supervised ECG pretraining, introducing a dual-masking strategy to capture intra- and inter-lead dependencies. To overcome the above challenges, we utilize a pre-trained CBraMod encoder for EEG and pre-train a symmetric ECG encoder, equipping each modality with a rich foundational representation. These representations are then fused via simple embedding concatenation, allowing the classification head to learn cross-modal interactions, together enabling effective downstream learning despite limited multi-modal supervision. Evaluated on emotion recognition, our approach achieves near state-of-the-art performance, demonstrating that carefully designed physiological encoders, even with straightforward fusion, substantially improve downstream performance. These results highlight the potential of foundation-model approaches to harness the holistic nature of physiological signals, enabling scalable, label-efficient, and generalizable solutions for healthcare and affective computing.

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