Tian Sen, Wang Yijun, Dong Guoya, Pei Weihua, Chen Hongda
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1980-1983. doi: 10.1109/EMBC.2018.8512666.
Mental fatigue induced by long time mental work can cause deterioration in task performance and increase the risk of accidents. Recently, electroencephalogram (EEG)-based monitoring of mental fatigue has received increasing attention in the field of brain-computer interfaces (BCI). This study aims to employ EEG signals to measure the mental fatigue level by estimating reaction time (RT) in a psychomotor vigilance task (PVT). In a 36-hour sleep deprivation experiment, EEG data from 18 subjects were recorded every four hours in nine blocks, each consisting of three tasks: a 6-minute PVT task and two 3-minute resting states (eyes closed and eyes open). The mean RT in the PVT task showed a generally increasing trend during the 36-hour awake period, reflecting the increase of fatigue over time. For each task, multiple EEG features were extracted and selected to better estimate RT using a multiple linear regression (MLR) method. The correlation between predicted RT and actual RT was evaluated using a leave-one-subject-out (LOSO) validation strategy. After parameter optimization, EEG data from the PVT task obtained a mean correlation coefficient of $0.81 \pm 0.16$ across all subjects. Resting-state EEG data showed lower correlations (eyes-closed: $0.65 \pm 0.20$, eyes-open: $0.50 \pm 0.30)$ partially due to the involvement of shorter data lengths. These results demonstrate the feasibility and robustness of the EEG-based fatigue monitoring method, which could be potential for applications in operational environments.
长时间脑力劳动引起的精神疲劳会导致任务表现恶化，并增加事故风险。最近，基于脑电图（EEG）的精神疲劳监测在脑机接口（BCI）领域受到了越来越多的关注。本研究旨在通过估计心理运动警觉任务（PVT）中的反应时间（RT），利用EEG信号来测量精神疲劳水平。在一项36小时睡眠剥夺实验中，每四小时记录18名受试者的EEG数据，共九个时间段，每个时间段包括三项任务：一项6分钟的PVT任务和两项3分钟的休息状态（闭眼和睁眼）。在36小时的清醒期内，PVT任务中的平均RT总体呈上升趋势，反映出疲劳程度随时间增加。对于每项任务，提取并选择了多个EEG特征，以使用多元线性回归（MLR）方法更好地估计RT。使用留一法（LOSO）验证策略评估预测RT与实际RT之间的相关性。经过参数优化后，PVT任务的EEG数据在所有受试者中的平均相关系数为0.81±0.16。静息状态EEG数据的相关性较低（闭眼：0.65±0.20，睁眼：0.50±0.30），部分原因是数据长度较短。这些结果证明了基于EEG的疲劳监测方法的可行性和稳健性，该方法在操作环境中具有潜在的应用价值。
