Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building and model testing was done with high-level independent features from the frequency and temporal domains extracted from the computed ratios over time. Target features for model training were extracted from the subjective ratings collected after resting and task demand activities. Models were built by employing Logistic Regression, Support Vector Machines and Decision Trees and were evaluated with performance measures including accuracy, recall, precision and f1-score. The results indicate high classification accuracy of those models trained with the high-level features extracted from the alpha-to-theta ratios and theta-to-alpha ratios. Preliminary results also show that models trained with logistic regression and support vector machines can accurately classify self-reported perceptions of mental workload. This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.
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）
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）
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））
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）
