Being able to analyze and interpret signal coming from electroencephalogram (EEG) recording can be of high interest for many applications including medical diagnosis and Brain-Computer Interfaces. Indeed, human experts are today able to extract from this signal many hints related to physiological as well as cognitive states of the recorded subject and it would be very interesting to perform such task automatically but today no completely automatic system exists. In previous studies, we have compared human expertise and automatic processing tools, including artificial neural networks (ANN), to better understand the competences of each and determine which are the difficult aspects to integrate in a fully automatic system. In this paper, we bring more elements to that study in reporting the main results of a practical experiment which was carried out in an hospital for sleep pathology study. An EEG recording was studied and labeled by a human expert and an ANN. We describe here the characteristics of the experiment, both human and neuronal procedure of analysis, compare their performances and point out the main limitations which arise from this study.
EEG-VLM: A Hierarchical Vision-Language Model with Multi-Level Feature Alignment and Visually Enhanced Language-Guided Reasoning for EEG Image-Based Sleep Stage Prediction
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University（同济大学电子信息工程学院控制科学与工程系）；Tencent Youtu Lab（腾讯云视觉实验室）；Case Western Reserve University（凯斯西储大学）
A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging
Healthcare Technology and Innovation Center (HTIC), Indian Institute of Technology (IIT-M), India（医疗技术与创新中心（HTIC），印度理工学院马德拉斯分校（IIT-M））；Department of Electrical Engineering, Indian Institute of Technology, Madras (IITM), India（印度理工学院马德拉斯分校（IITM）电气工程系）
EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning
University of Applied Science and Technology（应用科技大学）；MZG Consulting（MZG咨询公司）；Institute of Electrical and Electronics Engineers（电气与电子工程师协会）；Baku State University（巴库国立大学）
Self-supervised EEG Representation Learning for Automatic Sleep Staging
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Relativity（Relativity公司）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Dreamento: an open-source dream engineering toolbox for sleep EEG wearables
Donders Institute for Brain, Behaviour and Cognition（唐德斯脑、行为与认知研究所）；Radboudumc（拉德堡德大学医学中心）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Netherlands Institute for Neuroscience（荷兰神经科学研究所）；Royal Netherlands Academy of Arts and Sciences（荷兰皇家艺术与科学院）
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Korea University（高丽大学）；University of Cambridge（剑桥大学）；Stanford University School of Medicine（斯坦福大学医学院）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗系统）；Korea University College of Medicine（高丽大学医学院）
Learning transferable human physiology from two million hours of sleep with SleepFM-2
Stanford University（斯坦福大学）；BrainCapture；Technical University of Denmark（丹麦技术大学）；Danish Center for Sleep Medicine（丹麦睡眠医学中心）；Hvidovre Hospital（希维多夫医院）；Pioneer Center for SMARTbiomed, National Research Center for the Working Environment（SMARTbiomed先锋中心，国家工作环境研究中心）
Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal
University of A Coruna（拉科鲁尼亚大学）；Instituto de Investigacion Biomedica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所）；Complexo Hospitalario Universitario de A Coruña (CHUAC)（拉科鲁尼亚大学医院）
SleepGMUformer: A gated multimodal temporal neural network for sleep staging
College of Computer Science and Technology, Qingdao University（青岛大学计算机科学与技术学院）；School of Information Science and Engineering, University of Jinan（济南大学信息科学与工程学院）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification
School of Computing, Wichita State University（维斯科州立大学计算机学院）；Computer Science and Engineering Department, University of Nevada, Reno（内华达大学里诺分校计算机科学与工程系）
Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning
Laboratoire d’imagerie, de vision et d’intelligence artificielle (LIVIA), École de technologie supérieure (ÉTS), Université du Québec（图像、视觉和人工智能实验室（LIVIA），工程学院（ÉTS），魁北克大学）；Ouraring Inc（Ouraring公司）；Center for Health Sciences, SRI International（健康科学中心，SRI国际）
Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics
Department of Biomedical Engineering, Southern University of Science and Technology（南方科技大学生物医学工程系）；Omni-Intelligence（全知智能）；Shenzhen Loop Area Institute（深圳河套学院）
