Scanlon Joanna Elizabeth Mary, Küppers Daniel, Büürma Anneke, Winneke Axel Heinrich
Fraunhofer Institute for Digital Media Technology, Branch Hearing, Speech and Audio Technology, Oldenburg, Germany.
Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany.
Front Neuroergon. 2025 Mar 12;6:1542379. doi: 10.3389/fnrgo.2025.1542379. eCollection 2025.
Decline 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.
Twenty-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.
Alpha, 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.
These 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.
疲劳导致的警觉性下降是交通安全中普遍关注的问题。部分自动驾驶（PAD）系统可以辅助驾驶，但随着时间的推移，由于任务参与度降低，会降低驾驶员的警觉性。移动脑电图（EEG）解决方案可以在车辆行驶时获取神经信息。本研究的目的是调查与警觉性相关的行为和大脑活动（即α、β和θ波功率）在PAD驾驶和手动驾驶之间的差异，以及随时间的变化，以及如何使用两种不同的EEG系统检测这些影响。
28名参与者进行了两项1小时的模拟驾驶任务，同时佩戴标准的24通道EEG帽和新开发的、不显眼且易于应用的10通道移动EEG传感器网格系统。一种场景需要手动控制车辆（手动驾驶），而另一种场景只需要监控车辆（PAD驾驶）。此外，还获取了车道偏离、闭眼百分比（PERCLOS）以及工作量、疲劳和压力的主观评分。
在PAD驾驶条件下，EEG的α、β和θ波功率以及PERCLOS更高，并且在两种条件下均随时间增加。两种EEG系统中都出现了相同的EEG频谱效应。在手动驾驶条件下，作为驾驶性能指标的车道偏离随时间增加。
这些影响表明，驾驶过程中疲劳和警觉性下降会随着时间显著增加，并且与PAD驾驶相关的疲劳和警觉性下降总体水平更高。EEG测量比行为测量更早地显示出显著影响，表明EEG可能比行为驾驶测量更快地检测到警觉性下降。这种新型的移动EEG网格系统可用于评估和改进现场的驾驶员监测系统，甚至在未来可作为额外的传感器，向驾驶员告知其警觉性水平的关键变化。除了驾驶之外，这种EEG传感器网格的其他应用领域是警觉性监测至关重要的安全关键工作环境。
