The generalization and robustness of an electroencephalogram (EEG)-based computer aided diagnostic system are crucial requirements in actual clinical practice. To reach these goals, we propose a new EEG representation that provides a more realistic view of brain functionality by applying multi-instance (MI) framework to consider the non-stationarity of the EEG signal. The non-stationary characteristic of EEG is considered by describing the signal as a bag of relevant and irrelevant concepts. The concepts are provided by a robust representation of homogenous segments of EEG signal using spatial covariance matrices. Due to the nonlinear geometry of the space of covariance matrices, we determine the boundaries of the homogeneous segments based on adaptive segmentation of the signal in a Riemannian framework. Each subject is described as a bag of covariance matrices of homogenous segments and the bag-level discriminative information is used for classification. To evaluate the performance of the proposed approach, we examine it in attention deficit hyperactivity/bipolar mood disorder detection and depression/normal diagnosis applications. Experimental results confirm the superiority of the proposed approach, which is gained due to the robustness of covariance descriptor, the effectiveness of Riemannian geometry, and the benefits of considering the inherent non-stationary nature of the brain.
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
Université Bretagne Sud（布列塔尼南部大学）；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）；Criteo AI Lab（Criteo人工智能实验室）；Université de Rouen（鲁昂大学）；IMT Atlantique（大西洋国立高等矿业电信学校）；CNRS（法国国家科学研究中心）
Towards Robust EEG Decoding Based on Riemannian Self-Attention
School of Artificial Intelligence and Computer Science（人工智能与计算机科学学院）；Jiangnan University（江南大学）；Department of Information Engineering and Computer Science（信息工程与计算机科学系）；University of Trento（特伦托大学）；Centre for Vision, Speech and Signal Processing (CVSSP)（视觉、语音与信号处理中心）；University of Surrey（萨里大学）
SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG
Nara Institute of Science and Technology (NAIST)（奈良科学技术大学）；Department of Dynamic Brain Imaging, ATR（动态脑成像部门，ATR）；Center for Advanced Intelligence Project, RIKEN（先进智能项目中心，RIKEN）
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
RIKEN Center for Advanced Intelligence Project (RIKEN AIP)（理化学研究所先进智能项目中心）；Advanced Telecommunications Research Institute International (ATR)（国际电气通信基础技术研究所）
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Birmingham（伯明翰大学）；Beijing Normal University（北京师范大学）；Zhejiang University（浙江大学）
Random matrix theory improved Fréchet mean of symmetric positive definite matrices
Université Paris Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；CentraleSupélec（中央理工-高等电力学院）；Université Savoie Mont Blanc（萨瓦勃朗峰大学）；Huawei Paris Research Center（华为巴黎研究中心）
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection
School of Artificial Intelligence and Computer Science, Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education（江南大学人工智能与计算机科学学院，智能医疗技术工程研究中心，教育部）；School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University（上海理工大学计算机与信息工程学院，人工智能研究院）；School of Internet of Things Engineering, Wuxi University（无锡大学物联网工程学院）
A Unified SPD Token Transformer Framework for EEG Classification: Systematic Comparison of Geometric Embeddings
Independent Researcher（独立研究者）；Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan（国家阳明交通大学电子物理系）；Department of Mathematics, Boise State University, Boise, ID, USA（数学系，博伊州立大学）
Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
Department of Biotechnology（生物技术系）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Department of Data Science and Artificial Intelligence（数据科学与人工智能系）
