发表机构
Peking University; Chinese Institute for Brain Research (CIBR)（北京大学; 北京脑科学与类脑研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出语言引导的多模态基础模型METIS，通过统一语言-信号对齐实现零样本多任务脑信号分析，在大型语料库预训练后，性能超越通用模型20.9%以上，并达到或超过监督模型。
AI 中文摘要
脑信号分析对于神经科学研究和临床诊断至关重要，然而当前方法面临关键局限性。端到端模型需要针对特定任务进行重新训练，且泛化能力有限；预训练模型缺乏语义深度，并且仍然依赖大量微调。与此同时，通用多模态基础模型虽然在其他领域表现强大，但由于表征错位和缺乏领域知识，难以解释脑信号。本研究通过统一的语言-信号对齐框架，引入了一种用于零样本和多任务脑信号分析的多模态基础模型（METIS）。METIS在迄今为止最大且最多样化的脑信号语料库上进行预训练，该语料库包含来自20个数据集的超过11,000名受试者的70,000多小时记录。在涵盖12个数据集的全面零样本评估中，METIS在平均准确率上领先最佳通用模型超过20.9%。值得注意的是，在没有任何微调的情况下，METIS的性能达到或超过了有监督的、任务特定的模型。此外，METIS展现出卓越的数据效率和强大的泛化能力，在少样本设置中平均AUROC优势超过16.0%，在跨数据集迁移中达到15.9%。这项工作为通用脑信号分析建立了新范式，为下一代神经技术铺平了道路。
英文摘要
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 multimodal foundation model for zero-shot and multi-task 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.
Comments38 pages, 7 main figures and 20 supplementary figures; includes Supporting Information. Code: https://github.com/mingzhi-c/metis-brain-signal-foundation-model
Journal refAdvanced Intelligent Systems, e70486 (2026)
