Brain foundation models (BFMs) have emerged as a transformative paradigm in computational neuroscience, offering a revolutionary framework for processing diverse neural signals across different brain-related tasks. These models leverage large-scale pre-training techniques, allowing them to generalize effectively across multiple scenarios, tasks, and modalities, thus overcoming the traditional limitations faced by conventional artificial intelligence (AI) approaches in understanding complex brain data. By tapping into the power of pretrained models, BFMs provide a means to process neural data in a more unified manner, enabling advanced analysis and discovery in the field of neuroscience. In this survey, we define BFMs for the first time, providing a clear and concise framework for constructing and utilizing these models in various applications. We also examine the key principles and methodologies for developing these models, shedding light on how they transform the landscape of neural signal processing. This survey presents a comprehensive review of the latest advancements in BFMs, covering the most recent methodological innovations, novel views of application areas, and challenges in the field. Notably, we highlight the future directions and key challenges that need to be addressed to fully realize the potential of BFMs. These challenges include improving the quality of brain data, optimizing model architecture for better generalization, increasing training efficiency, and enhancing the interpretability and robustness of BFMs in real-world applications.
BrainWave: A Brain Signal Foundation Model for Clinical Applications
Computer Science and Technology（计算机科学与技术）；Zhejiang University（浙江大学）；Chinese Academy of Sciences（中国科学院）；INSIDE Institute for Biological and Artificial Intelligence（生物与人工智能研究所）；Research Institute of Intelligent and Complex Systems（智能与复杂系统研究所）；Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Chicago（芝加哥大学）
Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks
ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）；Department of Electronic Engineering, Tsinghua University, China（清华大学电子工程系）；Department of Psychology, University of Cambridge, UK（剑桥大学心理学系）；Speech Hearing and Phonetic Sciences, University College London, UK（伦敦大学学院语音听力与语音科学系）；MRC Cognition and Brain Sciences Unit, University of Cambridge, UK（剑桥大学MRC认知与脑科学单位）
BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China（浙江大学工业控制技术状态重点实验室）；Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy（米兰理工学院电子、信息与生物工程系）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（中国科学院深圳先进技术研究所）
BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
Department of Radiation Oncology and Winship Cancer Institute, Emory University（放射肿瘤科和Winship癌症研究所，埃默里大学）；Department of Radiation and Cellular Oncology, The University of Chicago（放射肿瘤学与细胞肿瘤学部，芝加哥大学）；Department of Electrical and Computer Engineering, Georgia Institute of Technology（电气与计算机工程系，佐治亚理工学院）；Department of Biomedical Engineering, Georgia Institute of Technology（生物医学工程系，佐治亚理工学院）；Department of Biomedical Informatics, Emory University（生物医学信息学系，埃默里大学）；Department of Medical Physics, Memorial Sloan Kettering Cancer Center（医学物理系，纪念斯隆凯特琳癌症中心）
Diffusion-Guided Pretraining for Brain Graph Foundation Models
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA（电气与计算机工程系，莱维大学）；Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA（计算机科学与工程系，莱维大学）；Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学医学院）
AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applications
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；North China Electric Power University（华北电力大学）
EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
Department of Computer Science and Engineering, Kathmandu University（加德满都大学计算机科学与工程系）；Department of Electronics and Communication Engineering, Nepal Engineering College, Pokhara University（博卡拉大学尼泊尔工程学院电子与通信工程系）；Department of Applied Sciences and Chemical Engineering, Institute of Engineering, Pulchowk Campus, Tribhuvan University（特里布万大学工程学院普尔乔克校区应用科学与化学工程系）
From 100,000+ images to winning the first brain MRI foundation model challenges: Sharing lessons and models
CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；Lausanne University Hospital (CHUV)（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；Universite Catholique de Louvain（鲁汶大学）；University of Applied Sciences Western Switzerland (HES-SO)（瑞士西部应用科学与艺术大学）
CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Sun Yat-sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Karlsruher Institut fur Technologie（卡尔斯鲁厄理工学院）
BrainSymphony: A parameter-efficient multimodal foundation model for brain dynamics with limited data
Turner Institute for Brain and Mental Health（大脑与心理健康Turner研究所）；School of Psychological Sciences, Monash University（墨尔本大学心理学科学学院）；Cortical Labs（皮层实验室）；CIFAR Azrieli Global Scholars Program（CIFAR阿兹里埃利全球学者计划）
CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models
Department of Computer Science and Engineering（计算机科学与工程系）；Incheon National University（庆尚国立大学）；Department of Artificial Intelligence（人工智能系）；Inha University（Inha大学）；Center for Brain-Machine Interface（脑机接口中心）
Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model
Hertie Institute for AI in Brain Health, University of Tübingen（图宾根大学脑健康人工智能研究所）；Tübingen AI Center, University of Tübingen（图宾根大学图宾根人工智能中心）；Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy（柏林夏里特医学院心理治疗系）
CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
State Key Laboratory of Brain-machine Intelligence（脑机智能国家重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；Alibaba Group（阿里巴巴集团）；Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine（神经生物学系、附属心理健康中心及杭州第七人民医院，浙江大学医学院）；MOE Frontier Science Center for Brain Science and Brain-machine Integration（教育部脑科学与脑机集成前沿科学中心）
BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Beijing Jiaotong University（北京交通大学）
Few-Shot Continual Learning for 3D Brain MRI with Frozen Foundation Models
Independent Researcher, USA（美国独立研究者）；Indiana University at Bloomington, United States（印第安纳大学布卢明顿分校）；Independent Researcher, Taiwan（台湾独立研究者）；National Taiwan University, Taiwan（台湾国立大学）；Boise State University, United States（博伊西州立大学）；National Yang Ming Chiao Tung University, Taiwan（台湾阳明交通大学）
The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou（脑机智能国家重点实验室，浙江大学，杭州）；Department of Radiology, Brigham and Women’s Hospital, Mass General Brigham, Boston（放射科，布里洛妇女医院，马萨诸塞总医院，波士顿）；Harvard Medical School, Boston（哈佛医学院，波士顿）；Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin（医学工程与转化医学研究院，天津大学，天津）；School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu（信息与通信工程学院，电子科技大学，成都）；Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Mass General Brigham, Boston（精神病神经影像实验室，布里洛妇女医院，马萨诸塞总医院，波士顿）；Department of Psychiatry, Center for Morphometric Analysis, Massachusetts General Hospital, Boston（精神病科，形态分析中心，马萨诸塞总医院，波士顿）
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Emory University（埃默里大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Princeton University（普林斯顿大学）；Lehigh University（理海大学）
CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich（神经科学与医学研究所（INM-1），贾格尔研究中心）；Helmholtz AI, Research Centre Jülich（海德堡人工智能，贾格尔研究中心）；Institute for Brain Research, University Hospital Düsseldorf（脑研究所在杜塞尔多夫大学医院）；Institute of Computational Biology, Computational Health Center, Helmholtz Munich（计算生物学研究所，计算健康中心，海德堡慕尼黑）；Institute for Stroke and Dementia Research (ISD), LMU University Hospital, LMU Munich（中风与痴呆研究所以（ISD），慕尼黑大学医院，慕尼黑大学）；Computer Vision, Institute for Computational Visualistics, University of Koblenz（计算机视觉，计算视觉研究所，科布伦兹大学）
Prediction and Causality of functional MRI and synthetic signal using a Zero-Shot Time-Series Foundation Model
AGH University of Krakow, Poland（克拉科夫AGH大学）；Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France（里莫内神经科学研究所）
SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Key Laboratory of Brain-Inspired General Intelligence Large Model（北京脑启发通用智能大模型重点实验室）；Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology（脑认知与脑启发智能技术重点实验室）；The Hong Kong Polytechnic University（香港理工大学）
Segmentation of Gray Matters and White Matters from Brain MRI data
Department of Computer Science and Engineering（计算机科学与工程系）；Waseda University（早稻田大学）；Department of Neurosurgery（神经外科）；Teraoka Memorial Hospital（寺田纪念医院）；Department of Radiology（放射科）；Nippon Medical School Hospital（日本医学大学医院）；Tokyo Rosai Hospital（东京罗赛医院）
Achieving More Human Brain-Like Vision via Human EEG Representational Alignment
Department of Psychology, The Ohio State University（心理学系，俄亥俄州立大学）；McGovern Institute for Brain Research, Massachusetts Institute of Technology（脑科学研究所，麻省理工学院）；Department of Neuroscience, The University of Texas at Dallas（神经科学系，德克萨斯大学达拉斯分校）
