Bridging Brain with Foundation Models through Self-Supervised Learning

• URL: http://arxiv.org/abs/2506.16009v1
• Date: Thu, 19 Jun 2025 04:03:58 GMT
• Title: Bridging Brain with Foundation Models through Self-Supervised Learning
• Authors: Hamdi Altaheri, Fakhri Karray, Md. Milon Islam, S M Taslim Uddin Raju, Amir-Hossein Karimi,
• Abstract summary: Foundation models (FMs) have redefined the capabilities of artificial intelligence.<n>These advances present a transformative opportunity for brain signal analysis.<n>This survey systematically reviews the emerging field of bridging brain signals with foundation models.
• Score: 5.0273296425814635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models (FMs), powered by self-supervised learning (SSL), have redefined the capabilities of artificial intelligence, demonstrating exceptional performance in domains like natural language processing and computer vision. These advances present a transformative opportunity for brain signal analysis. Unlike traditional supervised learning, which is limited by the scarcity of labeled neural data, SSL offers a promising solution by enabling models to learn meaningful representations from unlabeled data. This is particularly valuable in addressing the unique challenges of brain signals, including high noise levels, inter-subject variability, and low signal-to-noise ratios. This survey systematically reviews the emerging field of bridging brain signals with foundation models through the innovative application of SSL. It explores key SSL techniques, the development of brain-specific foundation models, their adaptation to downstream tasks, and the integration of brain signals with other modalities in multimodal SSL frameworks. The review also covers commonly used evaluation metrics and benchmark datasets that support comparative analysis. Finally, it highlights key challenges and outlines future research directions. This work aims to provide researchers with a structured understanding of this rapidly evolving field and a roadmap for developing generalizable brain foundation models powered by self-supervision.

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