Journal ArticleDOI
Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks
Georgios N. Dimitrakopoulos,Ioannis Kakkos,Zhongxiang Dai,Hongtao Wang,Kyriakos N. Sgarbas,Nitish V. Thakor,Anastasios Bezerianos,Yu Sun +7 more
- Vol. 26, Iss: 4, pp 740-749
TLDR
The topological alterations of functional brain networks in the theta band of electroencephalography data from 40 male subjects undergoing two distinct fatigue-inducing tasks are investigated to demonstrate the feasibility of using functional connectivity as neural biomarkers for applicable fatigue monitoring.Abstract:
Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue. In this paper, we investigated the topological alterations of functional brain networks in the theta band (4 - 7 Hz) of electroencephalography (EEG) data from 40 male subjects undergoing two distinct fatigue-inducing tasks: a low-intensity one-hour simulated driving and a high-demanding half-hour sustained attention task [psychomotor vigilance task (PVT)]. Behaviorally, subjects demonstrated a robust mental fatigue effect, as reflected by significantly declined performances in cognitive tasks prior and post these two tasks. Furthermore, characteristic path length presented a positive correlation with task duration, which led to a significant increase between the first and the last five minutes of both tasks, indicating a fatigue-related disruption in information processing efficiency. However, significantly increased clustering coefficient was revealed only in the driving task, suggesting distinct network reorganizations between the two fatigue-inducing tasks. Moreover, high accuracy (92% for driving; 97% for PVT) was achieved for fatigue classification with apparently different discriminative functional connectivity features. These findings augment our understanding of the complex nature of fatigue-related neural mechanisms and demonstrate the feasibility of using functional connectivity as neural biomarkers for applicable fatigue monitoring.read more
Citations
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Journal ArticleDOI
Complex networks and deep learning for EEG signal analysis
TL;DR: The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis, and develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition.
Journal ArticleDOI
Mental Workload Drives Different Reorganizations of Functional Cortical Connectivity Between 2D and 3D Simulated Flight Experiments
Ioannis Kakkos,Georgios N. Dimitrakopoulos,Lingyun Gao,Yuan Zhang,Peng Qi,George K. Matsopoulos,Nitish V. Thakor,Anastasios Bezerianos,Yu Sun +8 more
TL;DR: It is found that increased alpha band efficiencies and beta band local efficiency were associated with elevated mental workload levels, while beta band global efficiency exhibited distinct development trends between 2D and 3D interfaces.
Journal ArticleDOI
A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems
TL;DR: In this article, the authors reviewed the applications of EEG features and deep learning approaches in driver drowsiness detection, and discussed the open challenges and opportunities in improving driver Drowsiness Detection based on EEG.
Journal ArticleDOI
Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome
Peng Qi,Hua Ru,Lingyun Gao,Xiaobing Zhang,Tianshu Zhou,Yu Tian,Nitish V. Thakor,Anastasios Bezerianos,Jinsong Li,Yu Sun,Yu Sun +10 more
TL;DR: A review of connectome studies on mental fatigue can be found in this article, where a brief introduction to neuroimaging studies and the brain connectome is provided, followed by a thorough overview of the connections among brain regions.
Journal ArticleDOI
A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics
TL;DR: The mean phase coherence method, based on the “phase-locking value,” was the most frequently used functional estimation technique in the reviewed studies and the unweighted functional brain network has received substantially more attention in the literature than the weighted network.
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