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）
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl, Mohamed Kari, Ralf Mikut, Albrecht Schmidt, Ozan Özdenizci
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This becomes a more challenging problem when privacy-preserving representation learning is of interest such as in clinical settings. To that end, we propose a multi-source learning architecture where we extract domain-invariant representations from dataset-specific private encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain alignment approach to impose domain-invariance for encoded representations, which outperforms state-of-the-art approaches in EEG-based emotion classification. Furthermore, representations learned in our pipeline preserve domain privacy as dataset-specific private encoding alleviates the need for conventional, centralized EEG-based deep neural network training approaches with shared parameters.
Learning aligned EEG representations with subject-specific encoders
University of São Paulo（圣保罗大学）；Université Paris-Saclay, Inria TAU team, LISN-CNRS（巴黎萨克雷大学，Inria TAU团队，LISN-CNRS）；Institut de neuromodulation, GHU Paris, psychiatrie et neurosciences, centre hospitalier Sainte-Anne, pôle hospitalo-universitaire 15, Université Paris Cité（神经调控研究所，GHU巴黎，精神病学与神经科学，圣安娜医院，大学医院中心15区，巴黎西岱大学）；Federal University of ABC (UFABC)（ABC联邦大学）；Yneuro；Swartz Center for Computational Neuroscience (SCCN), Institute for Neural Computation (INC), University of California San Diego（斯沃茨计算神经科学中心，神经计算研究所，加州大学圣地亚哥分校）
EEG Foundation Models: A Critical Review of Current Progress and Future Directions
Department of Computer Science & Engineering, University of Minnesota Twin Cities（计算机科学与工程系，明尼苏达大学双城分校）；Department of Bioengineering, University of Illinois at Urbana-Champaign（生物工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Neurology, Mayo Clinic（神经病学系，梅奥诊所）；Department of Neurology, University of Minnesota Twin Cities（神经病学系，明尼苏达大学双城分校）
Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals
School of Electrical and Electronic Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；School of Information Technology, Murdoch University（莫道克大学信息技术学院）；Daffodil International University（水仙国际大学）
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）
ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models
Brown University（布朗大学）；Stanford University（斯坦福大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；University of Oxford（牛津大学）；Nanyang Technological University（南洋理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG
Department of Artificial Intelligence in Healthcare, International Academia of Biomedical Innovation Technology（人工智能医疗系，国际生物医学创新技术学院）；National Taiwan University（台湾大学）；Neuro Industry Research, Neuro Industry, Inc.（神经产业研究，神经产业公司）；Department of Biomedical Engineering, University of Southern California（生物医学工程系，南加州大学）；Taiwan Artificial Intelligence Association（台湾人工智能协会）
Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning
The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Youjia Innov Tech Co., Ltd.（深圳优佳创新科技有限公司）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳－香港产学研研究院）
A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives
The College of Computer Science, Zhejiang University, Hangzhou, China.（浙江大学计算机科学学院）；The MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China.（脑科学与脑机接口前沿科学中心）；The Nanhu Brain-Computer Interface Institute, Hangzhou, China.（南湖脑机接口研究院）；The Qiushi Academy for Advanced Studies, Hangzhou, China.（启石先进研究院）
DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation
Aliyun School of Big Data, Changzhou University（阿里云大数据学院，长洲大学）；Department of Computing, Xi’an JiaoTong-Liverpool University（计算系，西安交通大学-利物浦大学）；Department of Computer Science, University of Liverpool（计算机科学系，利物浦大学）；Center for Artificial Intelligence in Agriculture, Fujian Agriculture and Forestry University（农业人工智能中心，福建农林大学）
Gecko: Fast Private Inference via Secure Public Encoder Offloading
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
IEFS-GMB: Gradient Memory Bank-Guided Feature Selection Based on Information Entropy for EEG Classification of Neurological Disorders
Institute of Artificial Intelligence, Heifei Comprehensive National Science Center（人工智能研究院）；School of Artificial Intelligence, Anhui University（人工智能学院）；McGovern Institute for Brain Research, Peking University（脑科学研究院）；Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University（磁共振研究中心）；Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University（多模态认知计算重点实验室）
