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首页> 外文期刊>Iranian journal of public health. >Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving
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Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving

机译:基于模拟驾驶过程中脑电图阿尔法功率变化的驾驶员心理疲劳检测

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Background: Driver fatigue is one of the major implications in transportation safety and accounted for up to 40% of road accidents. This study aimed to analyze the EEG alpha power changes in partially sleep-deprived drivers while performing a simulated driving task.Methods: Twelve healthy male car drivers participated in an overnight study. Continuous EEG and EOG records were taken during driving on a virtual reality simulator on a monotonous road. Simultaneously, video recordings from the driver face and behavior were performed in lateral and front views and rated by two trained observers. Moreover, the subjective self-assessment of fatigue was implemented in every 10-min interval during the driving using Fatigue Visual Analog Scale (F-VAS). Power spectrum density and fast Fourier transform (FFT) were used to determine the absolute and relative alpha powers in the initial and final 10 minutes of driving.Results: The findings showed a significant increase in the absolute alpha power (P = 0.006) as well as F-VAS scores during the final section of driving (P = 0.001). Meanwhile, video ratings were consistent with subjective self-assessment of fatigue.Conclusion: The increase in alpha power in the final section of driving indicates the decrease in the level of alertness and attention and the onset of fatigue, which was consistent with F-VAS and video ratings. The study suggested that variations in alpha power could be a good indicator for driver mental fatigue, but for using as a countermeasure device needed further investigations.
机译:背景:驾驶员疲劳是交通安全的主要隐患之一,占道路交通事故的40%。这项研究旨在分析部分睡眠不足的驾驶员在执行模拟驾驶任务时的脑电图阿尔法功率变化。方法:十二名健康的男性汽车驾驶员参加了一项过夜研究。在单调道路上的虚拟现实模拟器上行驶期间,记录了连续的EEG和EOG记录。同时,驾驶员侧面和行为的视频记录在侧面和正面进行,并由两名训练有素的观察者进行评分。此外,使用疲劳视觉模拟量表(F-VAS)在驾驶过程中每隔10分钟进行一次疲劳的主观自我评估。功率谱密度和快速傅里叶变换(FFT)用于确定驾驶的最初和最后10分钟的绝对和相对α功率。结果:研究结果表明绝对α功率也显着增加(P = 0.006)作为F-VAS在驾驶最后阶段的分数(P = 0.001)。同时,视频收视率与疲劳的主观自我评估相一致。结论:驾驶最后阶段的alpha功率增加表明警觉和注意水平的降低以及疲劳的发作,这与F-VAS一致和视频分级。该研究表明,α功率的变化可能是驾驶员精神疲劳的良好指标,但用作对策设备需要进一步研究。

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