A Language‐Guided Multimodal Foundation Model for Zero‐Shot and Multi‐Task Brain Signal Analysis

Brain signal analysis is essential for both neuroscience research and clinical diagnostics, yet current approaches face critical limitations. End‐to‐end models require task‐specific retraining and exhibit limited generalization, while pre‐trained models lack semantic depth and still depend on extensive fine‐tuning. Meanwhile, general‐purpose multimodal foundation models, though powerful in other domains, struggle to interpret brain signals due to representational misalignment and lack of domain knowledge. This study introduces a M ultimodal foundation mod E l for zero‐sho T and mult I ‐ta S k brain signal analysis ( METIS ) through a unified language‐signal alignment framework. METIS is pretrained on the largest and most diverse brain‐signal corpus to date, comprising over 70 000 h of recordings from more than 11 000 subjects across 20 datasets. In a comprehensive zero‐shot evaluation across 12 datasets, METIS outperformed the leading generalist model by over 20.9% in average accuracy. Remarkably, without any fine‐tuning, METIS's performance matches or exceeds that of supervised, task‐specific models. Furthermore, METIS demonstrates exceptional data efficiency and strong generalization, achieving an average AUROC advantage of over 16.0% in few‐shot settings and 15.9% in cross‐dataset transfer. This work establishes a new paradigm for general‐purpose brain signal analysis, paving the way for next‐generation neurotechnology. The code is available at https://github.com/mingzhi‐c/metis‐brain‐signal‐foundation‐model .