This study investigates brain connectivity and information flow during mental workload (MWL) by integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. Utilizing the N-back task to induce varying levels of MWL in 26 participants, we analyzed both functional and effective connectivity across 25 cortical regions derived from combined EEG and fNIRS signals. Functional connectivity was assessed using Pearson Correlation Coefficient (PCC), Phase Locking Value (PLV), and Magnitude Squared Coherence (MSC), while effective connectivity was evaluated using directed Directed Transfer Function (dDTF) and generalized Partial Directed Coherence (gPDC). Our findings reveal increased functional connectivity in frontal regions during higher MWL conditions (3-back compared to 0-back). Furthermore, effective connectivity analysis demonstrates a significant directional information flow from EEG to fNIRS, indicating a dominant influence of neural activity on hemodynamic responses. Statistical tests confirm significant differences in connectivity patterns between low and high MWL states. These results underscore the utility of EEG-fNIRS integration for characterizing brain network dynamics under varying cognitive demands and provide insights into neurovascular coupling mechanisms during mental workload.
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing
NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University（西溪大学工程学院先进神经技术中心）；School of Data Science, Xiamen University Malaysia（马来西亚厦门大学数据科学学院）；Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第二医院神经外科）；Department of Education and Correction, Zhejiang Gongchen Compulsory Isolated Detoxification Center（浙江省公检强制隔离戒毒所教育矫正部）；Zhejiang Liangzhu Compulsory Isolated Detoxification Center（浙江省良渚强制隔离戒毒所）
A Statistical Approach for Synthetic EEG Data Generation
College of Science and Engineering, James Cook University（科学与工程学院，詹姆斯库克大学）；College of Health Care Sciences, James Cook University（健康护理科学学院，詹姆斯库克大学）；College of Public Health, Medical, and Vet Sciences, James Cook University（公共卫生、医学与兽医学学院，詹姆斯库克大学）
Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
School of Computer Science, Technological University Dublin, Dublin, Ireland（计算机科学学院，都柏林技术大学，都柏林，爱尔兰）；Artificial Intelligence and Cognitive Load Lab, Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland（人工智能与认知负荷实验室，应用智能研究中心，计算机科学学院，都柏林技术大学，都柏林，爱尔兰）
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
COMSATS University Islamabad（伊斯兰堡COMSATS大学）；University of Okara（奥卡拉大学）；Ca’ Foscari University of Venice（威尼斯大学）；Ege University（埃格大学）；Université Paris-Est Créteil (UPEC)（巴黎东克雷泰伊大学（UPEC））
Improving EEG Decoding via Clustering-based Multi-task Feature Learning
Lehigh University（理海大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；South China University of Technology（华南理工大学）；Stanford University（斯坦福大学）；West China Hospital of Sichuan University（四川大学华西医院）；Guangdong University of Technology（广东工业大学）；Tokyo University of Agriculture and Technology（东京农工大学）；Nicolaus Copernicus University（尼古拉·哥白尼大学）
Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023
The Graduate School of Biomedical Engineering, UNSW Sydney（新南威尔士大学悉尼分校生物医学工程研究生院）；Data Science and Computational Intelligence Institute, University of Granada（格拉纳达大学数据科学与计算智能研究所）；UNSW Data Science Hub, The University of New South Wales（新南威尔士大学数据科学中心）；Centre for Applied Artificial Intelligence, Macquarie University（麦考瑞大学应用人工智能中心）；School of Computing, Macquarie University（麦考瑞大学计算学院）；School of Mathematics, Physics and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）；Department of Psychiatry, University of Cambridge（剑桥大学精神病学系）
Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality
Alfred E. Mann Department of Biomedical Engineering, University of Southern California（阿尔弗雷德·E·曼生物医学工程系，南加州大学）；Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California（明世系电气与计算机工程系，南加州大学）；Thomas Lord Department of Computer Science, University of Southern California（托马斯·洛德计算机科学系，南加州大学）；Brain and Creativity Institute, University of Southern California（创造力与大脑研究所，南加州大学）；Department of Psychology, University of Southern California（心理学系，南加州大学）；Information Science Institute, University of Southern California（信息科学研究所，南加州大学）；Department of Psychiatry and Behavioral Sciences, University of Southern California（精神病学与行为科学系，南加州大学）；Department of Psychology, University of California, Los Angeles（心理学系，加州大学洛杉矶分校）；Department of Linguistics, University of Southern California（语言学系，南加州大学）
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；East China University of Science and Technology（华东理工大学）；Wilmar International（丰益国际）
