Task-guided Spatiotemporal Network with Diffusion Augmentation for EEG-based Dementia Diagnosis and MMSE Prediction
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University（湖南生物信息省重点实验室，计算机科学与工程学院，中南大学）；Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University（新疆大数据与智能软件工程研究中心，软件学院，新疆大学）；School of Science and Engineering, University of Dundee（科学与工程学院，邓迪大学）；Department of Neurology, Xiangya Hospital, Central South University（神经内科，湘雅医院，中南大学）
Xiaoyu Zheng, Xu Tian, Bin Jiao, Kunbo Cui, Hanhe Lin, Lu Shen, Jin Liu
Patients with dementia typically exhibit cognitive impairment, which is routinely assessed using the Mini-Mental State Examination (MMSE). Concurrently, their underlying neurophysiological abnormalities are reflected in Electroencephalography (EEG), providing a basis for joint modeling. However, traditional multi-task approaches suffer from feature entanglement, which leads to inter-task interference when handling heterogeneous objectives.To address this challenge, we propose a task-guided spatiotemporal network (TGSN) with diffusion augmentation for EEG-based dementia diagnosis and MMSE prediction. Specifically, TGSN integrates a multi-band feature fusion module to capture complementary spectral information from EEG. Meanwhile, a pre-trained data augmentation module utilizing a diffusion process is introduced toincrease sample diversity. To model the complex spatiotemporal patterns of EEG, we propose a gated spatiotemporal attention module that captures long-range spatial dependencies and temporal dynamics. Moreover, we design a task-guided query module to achieve task-specific feature extraction, thereby mitigating task interference. The effectiveness of TGSN is evaluated on the XY02 dataset. Experimental results demonstrate that the proposed network outperforms several state-of-the-art methods, achieving classification accuracies of 97.78\% for Alzheimer's Disease (AD)/Frontotemporal Dementia (FTD) and 83.93\% for AD/FTD/Vascular Cognitive Impairment (VCI), which exceed the best baselines by 16.39\% and 8.28\%, respectively. In parallel, it reduces the RMSE for MMSE prediction to 1.93 and 2.38, achieving significant error reductions of 1.44 and 1.43 compared to the best baselines. Additionally, validation on the DS004504 dataset demonstrates strong cross-dataset generalization...
xEEGNet: Towards Explainable AI in EEG Dementia Classification
Department of Neuroscience, University of Padua（帕多瓦大学神经科学系）；Padua Neuroscience Center（帕多瓦神经科学中心）；Department of Information Engineering, University of Padua（帕多瓦大学信息工程系）；Department of Physics and Astronomy, University of Padua（帕多瓦大学物理与天文学系）；Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学信息系统研究所）
A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Faculty of Medicine Siriraj Hospital, Mahidol University（玛希隆大学诗里拉吉医学院）；Neurocognitive Disorders & Neural Computing Research Group（神经认知障碍与神经计算研究组）
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Department of Industrial and Systems Engineering, Rutgers University（鲁特大学工业与系统工程系）；Department of Psychiatry and Behavioral Sciences, University of Minnesota（明尼苏达大学精神病学与行为科学系）；Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University（国家阳明交通大学医学院脑科学研究所）
EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learning
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Institute of Neurological Diseases, Shenzhen Bay Laboratory, Shenzhen, China（深圳湾实验室神经疾病研究院）
Linguistic and Audio Embedding-Based Machine Learning for Alzheimer's Dementia and Mild Cognitive Impairment Detection: Insights from the PROCESS Challenge
Singapore University of Technology and Design（新加坡科技设计大学）；Hochschule für Musik Detmold（音乐学院Detmold）
Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease
University of California, San Diego（加利福尼亚大学圣地亚哥分校）；HippoScreen Neurotech Corp.（HippoScreen神经科技公司）；Swartz Center for Computational Neuroscience（Swartz计算神经科学中心）
Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
Stevens Institute of Technology（史蒂文斯理工学院）；Semcer Center for Healthcare Innovation（塞姆瑟医疗创新中心）；New Jersey Institute of Technology（新泽西理工学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University（国立阳明交通大学医学院脑科学研究所）
Behavior Score-Embedded Brain Encoder Network for Improved Classification of Alzheimer Disease Using Resting State fMRI
National Tsing Hua University（国立清华大学）；MOST Joint Research Center for AI Technology and All Vista Healthcare（科技部人工智能技术与全视野医疗联合研究中心）；National Health Research Institutes（国家卫生研究院）
准确检测痴呆症发作的能力在疾病的治疗中是重要的。临床上，阿尔茨海默病（AD）和轻度认知障碍（MCI）患者的诊断基于心理测试和脑成像（例如正电子发射断层扫描（PET）和解剖磁共振成像（MRI））的综合评估。在这项工作中，使用两个不同的数据集，我们提出了一个行为分数嵌入编码器网络（BSEN），集成定期管理的心理测试信息到编码过程中表示主题的静息状态fMRI数据的自动分类任务。BSEN基于3D卷积自动编码器结构，具有对比度损失，使用MiniMental State Examination（MMSE）和临床痴呆评级（CDR）的行为评分进行联合优化。我们提出的使用BSEN的分类框架实现了59.44%的总体识别准确率（3类分类：AD，MCI和健康对照），我们进一步提取了健康对照（HC）和AD患者之间最具区分力的区域。
