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）
Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approaches. However, in most situations, unlabeled data is available in abundance. By extracting information from this unlabeled data, it might be possible to reach competitive performance with deep neural networks despite limited access to labels. Approach. We investigated self-supervised learning (SSL), a promising technique for discovering structure in unlabeled data, to learn representations of EEG signals. Specifically, we explored two tasks based on temporal context prediction as well as contrastive predictive coding on two clinically-relevant problems: EEG-based sleep staging and pathology detection. We conducted experiments on two large public datasets with thousands of recordings and performed baseline comparisons with purely supervised and hand-engineered approaches. Main results. Linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available. Additionally, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena, such as age effects. Significance. We demonstrate the benefit of self-supervised learning approaches on EEG data. Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.
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）
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）
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）
Domain-Invariant Representation Learning from EEG with Private Encoders
Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Ludwig-Maximilians University Munich（慕尼黑路德维希-马克西米利安大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Graz University of Technology（格拉茨工业大学）；Silicon Austria Labs（奥地利硅实验室）
Large Language Models for EEG: A Comprehensive Survey and Taxonomy
Department of Computer Science and Engineering, Indian Institute of Technology Patna（计算机科学与工程系，印度理工学院帕纳布分校）；Infocomm Technology Cluster, Singapore Institute of Technology（信息通信技术集群，新加坡理工学院）
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）
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）
