Neural representations induced by naturalistic stimuli offer insights into how humans respond to stimuli in daily life. Understanding neural mechanisms underlying naturalistic stimuli processing hinges on the precise identification and extraction of the shared neural patterns that are consistently present across individuals. Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER). CL-SSTER utilizes contrastive learning to maximize the similarity of EEG representations across individuals for identical stimuli, contrasting with those for varied stimuli. The network employed spatial and temporal convolutions to simultaneously learn the spatial and temporal patterns inherent in EEG. The versatility of CL-SSTER was demonstrated on three EEG datasets, including a synthetic dataset, a natural speech comprehension EEG dataset, and an emotional video watching EEG dataset. CL-SSTER attained the highest inter-subject correlation (ISC) values compared to the state-of-the-art ISC methods. The latent representations generated by CL-SSTER exhibited reliable spatiotemporal EEG patterns, which can be explained by properties of the naturalistic stimuli. CL-SSTER serves as an interpretable and scalable framework for the identification of inter-subject shared neural representations in naturalistic neuroscience.
Interpretable EEG-to-Image Generation with Semantic Prompts
Pacific Parkinson’s Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada（太平洋帕金森研究中心、Djavad Mowafaghian脑健康中心、不列颠哥伦比亚大学，温哥华，加拿大）；Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran（电气与计算机工程系、伊朗德黑兰大学，德黑兰，伊朗）；Department of Medicine British Columbia, Vancouver, Canada（医学系、不列颠哥伦比亚大学，温哥华，加拿大）
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）
A large dataset of human EEG responses to short naturalistic videos for studying dynamic visual event processing
Freie Universität Berlin（柏林自由大学）；Charité – Universitätsmedizin Berlin（柏林夏里特医科大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；University of Amsterdam（阿姆斯特丹大学）
Atoms of Thought: Universal EEG Representation Learning with Microstates
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；School of Clinical Medicine, Tsinghua University（清华大学临床医学院）；Beijing Five Seasons Medical Technology Co., Ltd.（北京五 Seasons 医疗科技有限公司）
Investigating naturalistic hand movements by behavior mining in long-term video and neural recordings
University of Washington（华盛顿大学）；Center for Neurotechnology（神经技术中心）；University of Washington Institute for Neuroengineering（华盛顿大学神经工程研究所）；eScience Institute, University of Washington（华盛顿大学电子科学研究所）
From Signals to Trajectories: A Primer on Low-Dimensional Dynamics in Human EEG and MEG
McGill University Health Centre, McGill University（麦吉尔大学健康中心，麦吉尔大学）；Université de Montréal（蒙特利尔大学）；Institute of Applied Mathematics M. Picone, National Council of Research（M. Picone应用数学研究所，国家研究委员会）；MILA (Quebec Artificial Intelligence Institute)（MILA（魁北克人工智能研究所））；Centre UNIQUE (Quebec Neuro-AI Research Center)（UNIQUE中心（魁北克神经人工智能研究中心））；Northwestern University（西北大学）
REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
IMT Atlantique Lab-STICC UMR CNRS 6285（IMT Atlantique 实验室）；Psychology Department Université de Montréal（蒙特利尔大学心理学系）；Mila (Quebec AI research institute)（魁北克人工智能研究院）；UNIQUE (Quebec Neuro-AI research center)（魁北克神经人工智能研究中心）
Inter- and Intra-Subject Variability in EEG: A Systematic Survey
Vietnam Maritime University（越南海洋大学）；HAI-Smartlink Research Lab, Anchi STE Company（HAI-Smartlink研究实验室，Anchi STE公司）；Hanoi Architectural University（河内建筑大学）；Greenwich Vietnam, FPT University（格林威治越南，FPT大学）；Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（计算智能与脑机接口实验室，计算机科学学院，澳大利亚人工智能研究所，工程与信息科技学院，悉尼技术大学）
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）
Pseudo Channel: Time Embedding for Motor Imagery Decoding
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University（天津大学精密测试技术及仪器国家重点实验室）；Research Group Neuroinformatic, Faculty of Computer Science, University of Vienna（维也纳大学计算机科学学院神经信息学研究组）；University of Vienna（维也纳大学）
Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow
School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；School of Smart Health, Chongqing Polytechnic University of Electronic Technology（重庆电子工程职业大学智能健康学院）
