MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

TL;DR - MSBraM is a self-supervised EEG foundation model that learns hierarchical representations across multiple temporal scales. It improves transfer and generalization by combining local neural patterns with long-range temporal context.

• Uses a vector-quantized neural tokenizer to encode raw EEG at multiple temporal resolutions.
• Applies curriculum-based masked-code prediction, progressively integrating fine-grained and global patterns.
• Pretrained on more than 2,400 hours of EEG data.
• Outperformed other state-of-the-art pretrained models across 10 tasks on 12 public datasets.