随着煤矿设备智能机械化发展，煤矿井下设备操作要求矿工持续地将注意力集中在单调枯燥的操作任务和体力任务上。由于长时间轮班工作和重复性作业，矿工容易产生疲劳。即使矿工具有丰富井下作业经验，出现疲劳后仍然会导致矿工反应时间延迟、工作效率降低、注意力水平下降，而且疲劳后容易导致工作分心。然而，现有对煤矿从业者疲劳问题的研究多采用主观评定，缺乏对疲劳的客观评估，导致煤矿从业人员身体状况与岗位匹配度不佳，限制了煤矿管理者对矿工安全能力水平的提高。因此，本文通过实验研究，采集矿工下井前后的脑电数据进行分析，选取疲劳指标构建评估模型，旨在快速检测矿工疲劳状态。
       本文以夜班矿工为研究对象。首先进行脑电和行为测试实验研究。采用经典实验范式诱发事件相关电位(Event-related potentials，简称ERP)的认知成分P300，同时应用夜班矿工的主观疲劳量表。其次，根据实验数据分析夜班矿工的疲劳状态。采用配对t检验，分析夜班矿工下井前后主观疲劳数据和行为数据的差异；选取ERP成分P300作为关键指标，对矿工下井前后P300成分的波幅和潜伏期进行统计分析；筛选矿工上岗前的疲劳评估指标，包括主观评估得分、行为数据得分、脑电数据指标（波幅和潜伏期）、睡眠时间。最后，根据疲劳评估指标，构建多元线性回归模型，分析各指标的相关性并对矿工岗前疲劳进行筛查。
       从主观评价和行为指标分析结果发现，夜间矿工连续一线作业诱发了疲劳，操作反应时间明显延长。进一步比较上井后矿工ERP特征参数P300的波幅和潜伏期的变化情况，波幅明显降低，说明了矿工出现疲劳后脑部信息加工能力受到影响，对目标的注意力下降。因此，通过现场实验测试，应用事件相关电位（ERP）方法可用来研究矿工的疲劳程度；P300可以作为评价矿工疲劳的有效指标。本研究通过多元线性回归模型，提出了一种定量评估矿工岗前疲劳的方法。为了验证模型的正确性，进行了疲劳验证实验。实验结果表明，该疲劳评估方法通过比较矿工的主观和客观数据，可以成功地识别出岗前疲劳的矿工。

     With the development of intelligent mechanization of coal mine equipment, the operation of underground coal mine equipment requires the miners to continuously focus on the monotonous operation tasks and physical tasks.Miners are prone to fatigue because of long shift work and repetitive work.Even if the miners have rich underground working experience, fatigue will still lead to the miners' reaction time delay, reduced work efficiency, decreased attention level, and easily lead to work distraction after fatigue. However, the existing research on the fatigue problem of coal miners mostly adopts subjective assessment, and lacks objective assessment of fatigue, which leads to the poor matching degree between the physical conditions of coal miners and their posts, and restricts the improvement of the safety ability level of coal mine managers.Therefore, this paper collects EEG data before and after miners go into the well for analysis through experimental research.Fatigue indexes were selected to build an evaluation model to quickly detect the fatigue state of miners.
      This paper takes the night shift miners as the research object.Firstly, the EEG and behavioral tests were carried out.At the same time, subjective fatigue scales of night shift miners were collected by evoked cognitive component P300 (Event-Related Potentials, or ERP), a classical experimental paradigm.Secondly, the fatigue state of night shift miners is analyzed according to the experimental data.Paired t test was used to analyze the difference of subjective fatigue data and behavior data before and after night shift miners went down the well.The ERP component P300 was selected as the research object, and the amplitude and latency of P300 component before and after the miners went down the shaft were statistically analyzed.The fatigue evaluation indexes of miners before work were screened, including subjective evaluation score, behavioral data score, EEG data index (amplitude and latency), and sleep time.Finally, according to the fatigue evaluation indexes, a multiple linear regression model was established to analyze the correlation of each index and screen the pre-job fatigue of miners.
       The results of subjective evaluation and behavior index analysis show that the continuous front-line work at night induced fatigue, and the response time of operation was significantly prolonged.Furthermore, the amplitudes of the ERP characteristic parameter P300 and the changes of the latency period of the miners were further compared. The amplitudes of the ERP characteristic parameter P300 were significantly reduced, indicating that the brain information processing ability of the miners was affected after fatigue and their attention to the target was decreased.Therefore, the application of event-related potential (ERP) method can be used to study the fatigue degree of miners through field experiments.P300 can be used as an effective index to evaluate miners' fatigue.In this study, a quantitative method for evaluating pre-job fatigue of miners was established by using multiple linear regression model.In order to verify the correctness of the model, a fatigue verification experiment was carried out.The experimental results show that by comparing the subjective and objective data of the miners, the fatigue miners can be identified successfully.

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