Peng Yaoxing, Li Chunguang, Chen Qu, Zhu Yufei, Sun Lining
The Key Laboratory of Robotics System of Jiangsu Province School of Mechanical Electric Engineering Soochow University, Suzhou, China.
Mathematics Teaching and Research Section, Basic Course Department, Communication Sergeant School of Army Engineering University, Chongqing, China.
Front Neurosci. 2022 Mar 15;15:771056. doi: 10.3389/fnins.2021.771056. eCollection 2021.
The objective of this study was to investigate common functional near-infrared spectroscopy (fNIRS) features of mental fatigue induced by different tasks. In addition to distinguishing fatigue from non-fatigue state, the early signs of fatigue were also studied so as to give an early warning of fatigue.
fNIRS data from 36 participants were used to investigate the common character of functional connectivity network corresponding to mental fatigue, which was induced by psychomotor vigilance test (PVT), cognitive work, or simulated driving. To analyze the network reorganizations quantitatively, clustering coefficient, characteristic path length, and small worldness were calculated in five sub-bands (0.6-2.0, 0.145-0.600, 0.052-0.145, 0.021-0.052, and 0.005-0.021 Hz). Moreover, we applied a random forest method to classify three fatigue states.
In a moderate fatigue state: the functional connectivity strength between brain regions increased overall in 0.021-0.052 Hz, and an asymmetrical pattern of connectivity (right hemisphere > left hemisphere) was presented. In 0.052-0.145 Hz, the connectivity strength decreased overall, the clustering coefficient decreased, and the characteristic path length increased significantly. In severe fatigue state: in 0.021-0.052 Hz, the brain network began to deviate from a small-world pattern. The classification accuracy of fatigue and non-fatigue was 85.4%. The classification accuracy of moderate fatigue and severe fatigue was 82.8%.
The preliminary research demonstrates the feasibility of detecting mental fatigue induced by different tasks, by applying the functional network features of cerebral hemoglobin signal. This universal and robust method has the potential to detect early signs of mental fatigue and prevent relative human error in various working environments.
本研究旨在探究不同任务诱发的精神疲劳的常见功能近红外光谱（fNIRS）特征。除了区分疲劳与非疲劳状态外，还对疲劳的早期迹象进行了研究，以便对疲劳进行早期预警。
使用36名参与者的fNIRS数据来研究与精神疲劳相对应的功能连接网络的共同特征，精神疲劳由心理运动警觉测试（PVT）、认知工作或模拟驾驶诱发。为了定量分析网络重组，在五个子频段（0.6 - 2.0、0.145 - 0.600、0.052 - 0.145、0.021 - 0.052和0.005 - 0.021Hz）计算聚类系数、特征路径长度和小世界特性。此外，我们应用随机森林方法对三种疲劳状态进行分类。
在中度疲劳状态下：脑区之间的功能连接强度在0.021 - 0.052Hz整体增加，并呈现出不对称的连接模式（右半球>左半球）。在0.052 - 0.145Hz，连接强度整体下降，聚类系数下降，特征路径长度显著增加。在严重疲劳状态下：在0.021 - 0.052Hz，脑网络开始偏离小世界模式。疲劳与非疲劳的分类准确率为85.4%。中度疲劳与重度疲劳的分类准确率为82.8%。
初步研究表明，通过应用脑血红蛋白信号的功能网络特征来检测不同任务诱发的精神疲劳是可行的。这种通用且稳健的方法有潜力检测精神疲劳的早期迹象，并在各种工作环境中预防相关人为错误。
