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Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data

机译:疲劳驾驶员的大脑网络变化:基于有效连接性分析和书法数据的真实环境中的纵向研究

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摘要

The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.
机译:对驾驶过程中神经生理学变化的分析可以阐明疲劳的机理,而疲劳被认为是造成车辆事故的重要原因。警觉性的波动可以作为大脑网络连接的变化来研究,反映在所传递信息的方向和大小上。这些变化不仅由工作时间引起,而且由睡眠质量引起。在一项史无前例的为期5个月的纵向研究中,在近乎真实的环境中持续关注驾驶任务期间,收集了每日采样书法和EEG数据。使用与受试者反应时间相关的性能指数以及与睡眠质量相关的预测得分,我们通过分析大脑区域之间的动态耦合来识别驾驶员的疲劳程度,并研究其在不同频段的有效连通性的变化。 。研究结果支持这样的假设,即结合脑电图,行为和书法数据可以揭示警觉性下降的新特征。此外,使用定向措施(例如会聚交叉映射)可以促进疲劳对策设备的开发。

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