在当前技术快速发展的背景下，虽然我国煤矿生产取得了较好的发展，但总体安全形势仍不容乐观。研究显示，违章行为，尤其是习惯性违章，在人因事故中占较大比例，这类行为通常因抑制控制能力的下降而产生。因此，防止工人抑制控制能力衰退，减少习惯性违章行为，是降低事故率的关键。煤矿井下的低照度状况对个体的生理和心理有着不同程度的影响，但目前关于照度条件如何影响抑制控制能力的研究尚存在分歧，尚无利用脑电和行为数据预测个体抑制控制水平方面的研究。本文专注于不同照度环境下，个体在执行抑制控制任务时的抑制控制水平差异，旨在明确照度对个体抑制控制能力的影响，并开发预测个体抑制控制水平的机器学习模型，以降低习惯性违章行为，提升煤矿安全水平。

本文探讨了照度变化对个体抑制控制能力的影响机理，基于环境应激与行为心理学的理论，构建了照度对矿工抑制控制能力的影响机制模型。通过事件相关电位（ERP）技术，设计了模拟井下照度环境的Go/No-go任务，以收集个体的行为和生理数据。最后，通过对脑电活动特征及行为数据进行数据预处理和特征选择，开发了一个基于逻辑回归算法的预测模型，以预测不同照度条件下矿工的抑制控制水平。结果显示：（1）300Lux的照度诱发了更强的N200和P300波幅，表明高照度环境相比于低照度能够提高个体的警觉状态和注意力；（2）对照组与实验组被试的反应时与正确率均存在显著差异，进一步验证了井下环境中的低照度对矿工抑制控制能力具有显著影响，进而在抑制控制任务中引发更敏锐的认知抑制反应；（3）基于逻辑回归的抑制控制水平预测模型表现出高达90%的整体准确率，并在交叉验证中平均准确率达到85.70%，证明了其出色的预测能力和泛化性能；（4）不同预测特征的SHAP值散点图显示，C4电极点Go刺激诱发的N100波幅对预测高抑制控制水平最为重要，其波幅越大，对预测个体高水平抑制控制能力的贡献越大。此外，位于大脑的右侧额顶区域的C4电极的脑电活动成分（N100和P300），在Go/No-go任务中显示出对预测抑制控制水平具有重要作用。

综上所述，本文分析了井下照度对煤矿工人抑制控制能力的心理及神经机制，开发了基于逻辑回归算法的个体抑制控制水平预测模型，利用SHAP值解释了不同脑电特征对预测目标的重要性程度，为控制习惯性违章行为提供了新的研究视角和方法论。

In the context of rapid technological advancements, despite significant progress in China's coal mining production, the overall safety situation remains concerning. Research indicates that violations, especially habitual violations, account for a substantial proportion of human-factor accidents, often stemming from a decline in inhibitory control abilities. Therefore, preventing the decline in workers' inhibitory control capabilities and reducing habitual violations are key to lowering accident rates. The low illumination conditions in coal mines affect individuals' physiological and psychological states to varying degrees. However, current research on how illumination affects inhibitory control is inconclusive, and there is a lack of studies using electroencephalography (EEG) and behavioral data to predict levels of inhibitory control. This thesis focuses on the differences in inhibitory control levels under various illumination conditions during inhibitory control tasks, aiming to clarify the impact of illumination on individual inhibitory control abilities and to develop a machine learning model to predict these levels, thereby reducing habitual violations and enhancing mine safety.

This study explores the mechanisms by which illumination variations affect individual inhibitory control capabilities. Based on theories of environmental stress and behavioral psychology, a model of the impact of illumination on miners' inhibitory control capabilities was constructed. Using event-related potentials (ERPs) technology, a Go/No-go task simulating underground illumination conditions was designed to collect individual behavioral and physiological data. Subsequently, through data preprocessing and feature selection of EEG activity and behavioral data, a prediction model based on logistic regression was developed to predict miners' inhibitory control levels under different illumination conditions. The results indicate: (1) Illumination of 300 Lux induced stronger N200 and P300 amplitudes, suggesting that higher illumination improves individuals' alertness and attention compared to lower illumination; (2) Significant differences in reaction times and accuracy rates between control and experimental groups further validate the significant impact of low illumination in underground environments on miners' inhibitory control capabilities, leading to more acute cognitive inhibitory responses during inhibitory control tasks; (3) The inhibitory control level prediction model based on logistic regression demonstrated an overall accuracy of up to 90%, with an average accuracy of 85.70% in cross-validation, proving its excellent predictive capability and generalizability; (4) Scatter plots of SHAP values of different predictive features showed that the N100 amplitude induced by Go stimuli at the C4 electrode is most crucial for predicting high levels of inhibitory control, with larger amplitudes contributing more significantly to predicting high levels of individual inhibitory control. Additionally, the EEG components (N100 and P300) at the C4 electrode, located in the right frontal-parietal region of the brain, played an important role in predicting inhibitory control levels during the Go/No-go task.

In summary, this thesis analyzed the psychological and neural mechanisms of underground illumination on the inhibitory control abilities of coal miners, developed a logistic regression-based model for predicting individual inhibitory control levels, and utilized SHAP values to elucidate the importance of different EEG features in predicting the target, providing new perspectives and methodologies for controlling habitual violations.

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