Bridging Brain with Foundation Models through Self-Supervised Learning

TL;DR

This survey reviews how self-supervised learning enables foundation models to analyze brain signals effectively, overcoming traditional data limitations and addressing challenges like noise and variability.

Contribution

It systematically explores SSL techniques, brain-specific foundation models, multimodal integration, and evaluation benchmarks, providing a comprehensive roadmap for future research in this emerging field.

Findings

SSL techniques adapted for brain signals

Development of brain-specific foundation models

Identification of key challenges and future directions

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.