Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on their data without extensive feature engineering. The availability of large-scale datasets is a crucial aspect of allowing the experimentation of Deep Learning models. We are publishing the first large-scale clinical EEG dataset that simplifies data access and management for Deep Learning. This dataset contains eyes-closed EEG data prepared from a collection of 1,574 juvenile participants from the Healthy Brain Network. We demonstrate a use case integrating this framework, and discuss why providing such neuroinformatics infrastructure to the community is critical for future scientific discoveries.
Clinically Calibrated Machine Learning Benchmarks for Large-Scale Multi-Disorder EEG Classification
Department of Electronics；Electrical Communication Engineering Indian Institute of Technology, Kharagpur West Bengal, India；Department of Computer Science；Engineering Indian Institute of Technology, Kharagpur West Bengal, India；Subir Chowdhury School of Quality；Reliability Indian Institute of Technology, Kharagpur West Bengal, India
EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping
Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Berlin, Germany（柏林查理医院医学大学精神病与心理治疗系）；Hertie Institute for AI in Brain Health, University of Tübingen, Germany（图宾根大学健康人工智能研究所）
Window Stacking Meta-Models for Clinical EEG Classification
University of the West of England（西英格兰大学）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院NHS基金会信托）；North Bristol NHS Trust（北布里斯托尔NHS信托）
Uncovering the structure of clinical EEG signals with self-supervised learning
Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；InteraXon Inc.（InteraXon公司）；University of Helsinki（赫尔辛基大学）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）
Machine-Learning-Based Diagnostics of EEG Pathology
Medical Center – University of Freiburg（弗莱堡大学医学中心）；University of Freiburg（弗莱堡大学）；Machine Learning Lab（机器学习实验室）；Neuromedical AI Lab（神经医学人工智能实验室）；Neurorobotics Lab（神经机器人学实验室）
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals
University of Strathclyde（思克莱德大学）；SRM Institute of Science and Technology（SRM科学技术学院）；Sheffield Hallam University（谢菲尔德哈勒姆大学）；Dublin City University（都柏林城市大学）；University of the West of England（西英格兰大学）
Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets
University of Tübingen（蒂宾根大学）；Hertie Institute for AI in Brain Health (Hertie AI)（赫蒂脑健康人工智能研究所）；University Clinic Tübingen（蒂宾根大学医院）；Hertie Institute for Clinical Brain Research（赫蒂临床脑研究所）
NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-Informed Neural Network Framework for Electroencephalograph (EEG) Analysis and Motor Imagery Classification
University of Technology Sydney（新南威尔士大学悉尼分校）；Queensland University of Technology（昆士兰理工大学）
Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction
Department of Medical Informatics（医学信息学系）；UCLA（美国加利福尼亚大学洛杉矶分校）；Department of Cognitive Science（认知科学系）；UCSD（美国圣地亚哥大学）；David Geffen School of Medicine（大卫·盖弗医学院）；Mattel Children’s Hospital（马特尔儿童医院）
EEG-CLIP : Learning EEG representations from natural language descriptions
Medical Physics, Department of Diagnostic and Interventional Radiology（医学物理，诊断与介入放射科）；Medical Center—University of Freiburg, Faculty of Medicine University of Freiburg, Germany（弗赖堡大学医学中心—弗赖堡大学医学院，弗赖堡，德国）；Neuromedical A.I. Lab, Department of Neurosurgery（神经医学人工智能实验室，神经外科部门）；BrainLinks-BrainTools, IMBIT University of Freiburg, Germany（BrainLinks-BrainTools，IMBIT弗赖堡大学，德国）
Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
BioSerenity；Université de Lille（里尔大学）；CHU de Lille（里尔大学医院）；Hôpital Universitaire de Bruxelles（布鲁塞尔大学医院）；Université Libre de Bruxelles（布鲁塞尔自由大学）；Yale University School of Medicine（耶鲁大学医学院）；Université de Lorraine（洛林大学）；CHRU-Nancy（南锡大学地区医院）；CHU Grenoble Alpes（格勒诺布尔大学医院）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Jolt Capital
A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning
Indian Knowledge Systems and Mental Health Applications (IKSMHA) Center, Indian Institute of Technology Mandi（印度理工学院曼迪分校印度知识体系与心理健康应用中心）；School of Computing and Electrical Engineering, Indian Institute of Technology Mandi（印度理工学院曼迪分校计算与电气工程学院）
Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoders
BrainCapture；DTU Health Tech（技术大学丹麦健康技术）；DTU Compute（技术大学丹麦计算）；Department of Biomedical Data Science（生物医学数据科学系）；Department of Computer Science（计算机科学系）；Seer Medical（Seer医疗）；Filadelfia Epilepsy Hospital（菲拉德尔菲亚癫痫医院）；University Hospital of Copenhagen（哥本哈根大学医院）
Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy
Frankfurt Institute for Advanced Studies (FIAS)（法兰克福高等研究院）；Goethe University Frankfurt（法兰克福大学）；Center for Personalized Translational Epilepsy Research (CePTER)（个性化转化癫痫研究中心）
