Data Sheet 1_Mind the road: attention related neuromarkers during automated and manual simulated driving captured with a new mobile EEG sensor system.pdf

BackgroundDecline in vigilance due to fatigue is a common concern in traffic safety. Partially automated driving (PAD) systems can aid driving but decrease the driver's vigilance over time, due to reduced task engagement. Mobile EEG solutions can obtain neural information while operating a vehicle. The purpose of this study was to investigate how the behavior and brain activity associated with vigilance (i.e., alpha, beta and theta power) differs between PAD and manual driving, as well as changes over time, and how these effects can be detected using two different EEG systems.
MethodsTwenty-eight participants performed two 1-h simulated driving tasks, while wearing both a standard 24 channel EEG cap and a newly developed, unobtrusive and easy to apply 10 channel mobile EEG sensor-grid system. One scenario required manual control of the vehicle (manual) while the other required only monitoring the vehicle (PAD). Additionally, lane deviation, percentage eye-closure (PERCLOS) and subjective ratings of workload, fatigue and stress were obtained.
ResultsAlpha, beta and theta power of the EEG as well as PERCLOS were higher in the PAD condition and increased over time in both conditions. The same spectral EEG effects were evident in both EEG systems. Lane deviation as an index of driving performance in the manual driving condition increased over time.
ConclusionThese effects indicate significant increases in fatigue and vigilance decrement over time while driving, and overall higher levels of fatigue and vigilance decrement associated with PAD. The EEG measures revealed significant effects earlier than the behavioral measures, demonstrating that EEG might allow faster detection of decreased vigilance than behavioral driving measures. This new, mobile EEG-grid system could be used to evaluate and improve driver monitoring systems in the field or even be used in the future as additional sensor to inform drivers of critical changes in their level of vigilance. In addition to driving, further areas of application for this EEG-sensor grid are safety critical work environments where vigilance monitoring is pivotal.

研究背景：疲劳引发的警觉性下降是交通安全领域的常见关切。部分自动驾驶（Partially Automated Driving, PAD）系统能够辅助驾驶，但由于任务参与度降低，会随时间推移降低驾驶员的警觉性。移动式脑电图（Electroencephalogram, EEG）解决方案可在车辆行驶过程中获取神经信息。本研究旨在探究与警觉性相关的行为与脑活动（即α、β及θ频段功率）在部分自动驾驶与手动驾驶模式间的差异，以及其随时间的变化规律，并探讨如何通过两种不同的脑电图系统检测这些效应。

研究方法：28名参与者完成两项时长1小时的模拟驾驶任务，同时佩戴标准24通道脑电图帽与全新研发的、无干扰且易于佩戴的10通道移动式脑电图传感器网格系统。其中一项场景要求手动操控车辆（手动驾驶模式），另一项仅要求驾驶员监控车辆（部分自动驾驶模式）。此外，还采集了车道偏移数据、闭眼百分比（PERCLOS）以及工作负荷、疲劳与应激的主观评分。

研究结果：部分自动驾驶模式下的脑电图α、β及θ频段功率与闭眼百分比均更高，且两种模式下上述指标均随时间推移上升。两种脑电图系统均观测到了一致的脑电图频谱效应。手动驾驶模式下作为驾驶性能指标的车道偏移量随时间推移增加。

研究结论：上述结果表明，驾驶过程中疲劳程度与警觉性下降随时间显著加剧，且部分自动驾驶模式下的疲劳程度与警觉性下降整体水平更高。脑电图指标较行为学指标更早显现出显著效应，这表明脑电图或可比驾驶行为学指标更快地检测到警觉性下降。这套新型移动式脑电图传感器网格系统可用于评估并优化现场的驾驶员监测系统，未来甚至可作为额外传感器，向驾驶员提示其警觉性水平的关键变化。除驾驶场景外，该脑电图传感器网格系统的其他应用领域还包括亟需警觉性监测的安全关键型工作环境。