Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two steps. We first propose a new source separation technique which takes into account the autocorrelations (which may be time-varying) and time-varying variances of the sources. The causality in the envelopes is then discovered by exploiting a special kind of multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model. The resulting causal diagram gives the effective connectivity between the separated sources; in our experimental results on MEG data, sources with similar functions are grouped together, with negative influences between groups, and the groups are connected via some interesting sources.
BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）；Department of Electronic Engineering, Tsinghua University, China（清华大学电子工程系）；Department of Psychology, University of Cambridge, UK（剑桥大学心理学系）；Speech Hearing and Phonetic Sciences, University College London, UK（伦敦大学学院语音听力与语音科学系）；MRC Cognition and Brain Sciences Unit, University of Cambridge, UK（剑桥大学MRC认知与脑科学单位）
Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Picower Institute for Learning and Memory（皮考尔学习与记忆研究所）；MIT（麻省理工学院）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Diffusion-based translation between unpaired spontaneous premature neonatal EEG and fetal MEG
Laboratoire de Mathématiques de Lens (LML), UR 2462, Université d'Artois（Lens数学实验室（LML），UR 2462，阿托瓦大学）；Department of Internal Medicine IV, University Hospital of Tübingen（第四内科部门，图宾根大学医院）；IDM/fMEG Center of the Helmholtz Center Munich, University of Tübingen, German Center for Diabetes Research (DZD)（慕尼黑海德堡中心IDM/fMEG中心，图宾根大学，糖尿病研究德国中心（DZD））；Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Interfaculty Centre for Pharmacogenomics and Pharma Research, Eberhard Karls University Tübingen（药学研究所，药学与生物化学部门，药基因组学与药学研究跨学科中心，图宾根埃伯哈德·卡尔斯大学）
CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models
CRCV, University of Central Florida（CRCV，中央佛罗里达大学）；University of Central Florida（中央佛罗里达大学）；Department of MAE, University of Central Florida（中央佛罗里达大学机械与航空航天工程系）；Department of Neurology, Loma Linda University（洛马琳达大学神经病学系）
Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior
Anhui University（安徽大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Sanbo Brain Hospital Capital Medical University（首都医科大学三博脑科医院）；Peking University（北京大学）
Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Harvard-MIT Division of Health Sciences and Technology（哈佛-麻省理工卫生科学与技术部）；Institute for Medical Engineering, Research Campus STIMULATE, Otto-von-Guericke University（奥托·冯·格里克大学医学工程研究所（STIMULATE研究校区））；Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau（伊尔默瑙工业大学生物医学工程与信息学研究所）
EEG-to-Gait Decoding via Phase-Aware Representation Learning
College of Computing and Data Science, Nanyang Technological University, Singapore 639798（计算与数据科学学院，南洋理工大学，新加坡）；Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China（计算机科学与工程系，上海交通大学，上海）；Institute of Neural Engineering, Graz University of Technology, Graz, Austria（神经工程研究所，格拉茨技术大学，奥地利）；Centre of AI in Medicine (C-AIM), Nanyang Technological University, Singapore（医学人工智能中心（C-AIM），南洋理工大学，新加坡）
