Papadelis Christos, Chen Zhe, Kourtidou-Papadeli Chrysoula, Bamidis Panagiotis D, Chouvarda Ioanna, Bekiaris Evangelos, Maglaveras Nikos
Aristotle University of Thessaloniki, School of Medicine, Laboratory of Medical Informatics, PO Box 323, 54124 Thessaloniki, Greece.
Clin Neurophysiol. 2007 Sep;118(9):1906-22. doi: 10.1016/j.clinph.2007.04.031. Epub 2007 Jul 24.
The objective of this study is the development and evaluation of efficient neurophysiological signal statistics, which may assess the driver's alertness level and serve as potential indicators of sleepiness in the design of an on-board countermeasure system.
Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects exposed to real field driving conditions. A number of severe driving errors occurred during the experiments. The analysis was performed in two main dimensions: the macroscopic analysis that estimates the on-going temporal evolution of physiological measurements during the driving task, and the microscopic event analysis that focuses on the physiological measurements' alterations just before, during, and after the driving errors. Two independent neurophysiologists visually interpreted the measurements. The EEG data were analyzed by using both linear and non-linear analysis tools.
We observed the occurrence of brief paroxysmal bursts of alpha activity and an increased synchrony among EEG channels before the driving errors. The alpha relative band ratio (RBR) significantly increased, and the Cross Approximate Entropy that quantifies the synchrony among channels also significantly decreased before the driving errors. Quantitative EEG analysis revealed significant variations of RBR by driving time in the frequency bands of delta, alpha, beta, and gamma. Most of the estimated EEG statistics, such as the Shannon Entropy, Kullback-Leibler Entropy, Coherence, and Cross-Approximate Entropy, were significantly affected by driving time. We also observed an alteration of eyes blinking duration by increased driving time and a significant increase of eye blinks' number and duration before driving errors.
EEG and EOG are promising neurophysiological indicators of driver sleepiness and have the potential of monitoring sleepiness in occupational settings incorporated in a sleepiness countermeasure device.
The occurrence of brief paroxysmal bursts of alpha activity before severe driving errors is described in detail for the first time. Clear evidence is presented that eye-blinking statistics are sensitive to the driver's sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.
本研究的目的是开发和评估有效的神经生理信号统计方法，这些方法可评估驾驶员的警觉水平，并在车载对策系统设计中作为困倦的潜在指标。
记录了处于睡眠剥夺状态的受试者在实际道路驾驶条件下的多通道脑电图（EEG）、眼电图（EOG）、肌电图（EMG）和心电图（ECG）。实验过程中出现了一些严重的驾驶失误。分析主要在两个维度上进行：宏观分析，估计驾驶任务期间生理测量的持续时间演变；微观事件分析，关注驾驶失误之前、期间和之后生理测量的变化。两名独立的神经生理学家对测量结果进行了视觉解读。使用线性和非线性分析工具对脑电图数据进行了分析。
我们观察到在驾驶失误之前出现了短暂的阵发性α活动爆发，并且脑电图通道之间的同步性增加。驾驶失误前，α相对带宽比（RBR）显著增加，量化通道间同步性的交叉近似熵也显著降低。定量脑电图分析显示，在δ、α、β和γ频段，RBR随驾驶时间有显著变化。大多数估计的脑电图统计量，如香农熵、库尔贝克-莱布勒熵、相干性和交叉近似熵，都受到驾驶时间的显著影响。我们还观察到，随着驾驶时间的增加，眨眼持续时间发生了变化，并且在驾驶失误之前眨眼次数和持续时间显著增加。
脑电图和眼电图是有前景的驾驶员困倦神经生理指标，有潜力在纳入困倦对策装置的职业环境中监测困倦。
首次详细描述了严重驾驶失误之前短暂阵发性α活动爆发的情况。明确证据表明，眨眼统计量对驾驶员的困倦敏感，在设计高效且对驾驶员友好的困倦检测对策装置时应予以考虑。
