当前，煤矿安全生产形势严峻复杂，重特大事故呈反弹趋势。在人因失误长期主导煤矿事故诱因的背景下，强化矿工风险感知能力已成为提升安全管理效能的关键路径。然而，矿工群体日益突显的老龄化问题所带来的生理机能衰退与认知能力减弱，以及部分矿工表现出的高感觉寻求倾向所诱发的冒险行为，正共同制约着风险感知能力的有效提升。因此，本文从神经安全管理学角度出发，通过探究矿工年龄与感觉寻求对风险感知的影响，识别矿工的风险感知能力，以期提出异质性的矿工风险感知能力提升策略，从而推动煤矿安全管理向科学化、精准化和个性化发展。

首先，基于国内外的研究现状，系统梳理了风险感知和感觉寻求的理论基础，明确了矿工年龄段的划分及其个体特征差异，并从多维度提出了矿工年龄与感觉寻求对风险感知影响的研究假设。同时，编制了矿工感觉寻求量表，设计了矿工风险感知实验，运用功能性近红外光谱技术（functional Near-Infrared Spectroscopy, fNIRS）采集了77名矿工风险感知的实验数据，采用MATLAB的NIRS-KIT工具箱对fNIRS数据进行了预处理。其次，运用统计分析和脑成像等方法，检验了矿工感觉寻求量表的信效度，剖析了不同年龄矿工的感觉寻求倾向，分析了矿工年龄与感觉寻求对风险感知的影响，并进一步探究了两者对风险感知的交互效应。最后，通过优选提取风险感知特征建立了不同特征组合的数据集，采用K-Means聚类算法实现了矿工风险感知能力的分级，构建了基于BP神经网络、PSO-BP神经网络和GA-BP神经网络的矿工风险感知能力识别模型。在此基础上，从矿工年龄与感觉寻求的视角出发，提出了针对不同特征群体的差异化风险感知能力提升策略。

研究结果表明：（1）年轻矿工的感觉寻求倾向显著高于年长矿工。年轻矿工在工作中表现出较强的对新鲜和刺激的需求，年长矿工通常更注重工作中的稳定性和安全性。（2）矿工年龄与感觉寻求对风险感知存在显著影响。在年龄维度，年长矿工的风险等级正确率较高，风险数量和风险等级反应时间较长，大脑激活程度较低且激活区域相对有限，在任务中依赖高效的认知策略。年轻矿工呈现出相反特点，通过投入更多的认知资源弥补经验不足的劣势。在感觉寻求维度，高感觉寻求矿工风险等级正确率较低，风险数量反应时间较短，大脑激活程度较高，倾向于主动探索和快速反应。低感觉寻求矿工表现出相反的特点，倾向于采用保守策略。（3）PSO-BP神经网络模型在风险感知能力识别中表现出较高的识别精度。PSO-BP神经网络模型的识别准确率高达95.831%，精准率、召回率与F1值均高于90%，能够较好的识别矿工的风险感知能力。

研究结果进一步扩展了神经安全管理学的理论体系，推动了功能性近红外光谱技术在煤矿安全领域的深入应用，为煤矿企业制定精准化安全管理策略以及搭建矿工风险感知能力预警平台提供了理论支撑和实践参考。

The current safety situation in coal mine production is severe and complex, with a rebound trend in major and catastrophic accidents. Against the long-standing backdrop where human error remains the dominant cause of coal mine accidents, enhancing miners’ risk perception ability has become a key pathway to improving the effectiveness of safety management. However, the physiological decline and cognitive weakening caused by the increasingly aging mining workforce, along with the risk-taking behaviors induced by high sensation seeking tendencies among certain miners, are jointly constraining the effective enhancement of risk perception capability. Therefore, from the perspective of neurosafety management, this study investigates the influence of miners’ age and sensation seeking on risk perception, to identify miners’ risk perception abilities and propose heterogeneous strategies for enhancing such abilities, thereby promoting the scientific, precise, and personalized development of coal mine safety management.

Firstly, based on the current state of domestic and international research, the theoretical foundations of risk perception and sensation seeking were systematically reviewed, the classification of miners’ age groups and their characteristics were clarified, and multidimensional research hypotheses regarding the influence of age and sensation seeking on risk perception were proposed. At the same time, a sensation seeking scale tailored for miners was developed, and a risk perception experiment was designed. functional Near-Infrared Spectroscopy (fNIRS) was employed to collect experimental data on risk perception from 77 miners, and the fNIRS data were preprocessed using the NIRS-KIT toolbox in MATLAB. Secondly, statistical analysis and brain imaging methods were applied to test the reliability and validity of the miners’ sensation seeking scale, to analyze the sensation seeking tendencies of miners of different ages, to examine the effects of miners’ age and sensation seeking on risk perception, and to explore further the interaction effects between the two variables on risk perception. Finally, by optimally extracting features related to risk perception, datasets with different feature combinations were constructed. The K-Means clustering algorithm was used to categorize miners’ risk perception abilities. Recognition models for miners’ risk perception abilities were developed based on the BP neural network, the PSO-BP neural network, and the GA-BP neural network. On this basis, from the perspective of miners’ age and sensation seeking, differentiated strategies for improving risk perception capabilities were proposed for groups with different characteristics.

The research results indicate: (1) Younger miners exhibit significantly higher sensation seeking tendencies than older miners. Younger miners demonstrate a stronger demand for novelty and stimulation in their work, whereas older miners generally place more emphasis on stability and safety. (2) Both miners’ age and sensation seeking have significant effects on risk perception. In terms of age, older miners show higher accuracy in judging risk levels, longer response times to the number and level of risks, lower brain activation, and relatively limited activation regions, relying on efficient cognitive strategies during tasks. Younger miners exhibit the opposite characteristics, compensating for their lack of experience by investing more cognitive resources. In terms of sensation seeking, high sensation seeking miners show lower accuracy in judging risk levels, shorter response times to the number of risks, higher levels of brain activation, and tend to engage in proactive exploration and rapid responses. Low sensation seeking miners display opposite traits, tending to adopt conservative strategies. (3) The PSO-BP neural network model performs with high recognition accuracy in identifying risk perception ability. The recognition accuracy of the PSO-BP model reaches 95.831%, with precision, recall, and F1-score all exceeding 90%, demonstrating the model’s excellent robustness and generalization capability.

The research results further expand the theoretical system of neurosafety management, promote the in-depth application of functional Near-Infrared Spectroscopy in the field of coal mine safety, and provide theoretical support and practical reference for coal enterprises to develop precise safety management strategies and build early-warning platforms for miners’ risk perception abilities.

[1]王双明, 刘浪, 赵玉娇, 等. “双碳”目标下赋煤区新能源开发——未来煤矿转型升级新路径 [J]. 煤炭科学技术, 2023, 51(1): 59-79.

[2]刘原奇. 2023-2024年全球煤炭市场研究与趋势分析 [J]. 中国煤炭, 2024, 50(7): 164-169.

[3]肖方. 王祥喜率工作组紧急赶赴阿拉善左旗露天煤矿坍塌事故现场并指导救援处置工作 [J]. 中国消防, 2023, (2): 5.

[4]国家统计局. 中华人民共和国2023年国民经济和社会发展统计公报 [J]. 中国统计, 2024, (3): 4-21.

[5]Chen H, Qi H, Feng Q. Characteristics of direct causes and human factors in major gas explosion accidents in Chinese coal mines: Case study spanning the years 1980–2010 [J]. Journal of Loss Prevention in the Process Industries, 2013, 26(1): 38-44.

[6]田水承, 王清焱, 林维, 等. 矿工安全态度与煤矿险兆事件影响关系研究 [J]. 安全与环境学报, 2024, 24(6): 2321-2328.

[7]Clarke S, T. Robertson I. A meta‐analytic review of the Big Five personality factors and accident involvement in occupational and non‐occupational settings [J]. Journal of Occupational and Organizational Psychology, 2011, 78(3): 355-376.

[8]李乃文, 王春迪, 牛莉霞. 高危岗位矿工个体风险感知影响因素仿真研究 [J]. 中国安全生产科学技术, 2015, 11(9): 184-190.

[9]朱翠明. 中国现代化进程中的人口老龄化问题与应对研究 [D]. 长春: 吉林大学, 2022.

[10]国家统计局. 2023年全国农民工监测调查报告 [J]. 建筑, 2024, (5): 60-63.

[11]Margolis K A. Underground coal mining injury: A look at how age and experience relate to days lost from work following an injury [J]. Safety Science, 2010, 48(4): 417-421.

[12]宇佳利, 王磊. 认知老化的发生机制及影响因素研究进展 [J]. 中国老年学杂志, 2018, 38(18): 4595-4598.

[13]高鸿纲. 年长员工工作活力及其影响因素研究 [D]. 武汉: 中南财经政法大学, 2023.

[14]于凯, 邢强. 感觉寻求对风险决策影响的认知神经机制 [J]. 广州大学学报(社会科学版), 2015, 14(4): 22-28.

[15]Li Z, Man S S, Chan A H S, et al. Integration of theory of planned behavior, sensation seeking, and risk perception to explain the risky driving behavior of truck drivers [J]. Sustainability, 2021, 13(9): 5214.

[16]Bauer R A. Consumer behavior as risk taking [J]. Marketing: Critical Perspectives on Business and Management, 1967, 593: 13-21.

[17]Cunningham S. The major dimensions of perceived risk [J]. Risk Taking and Information Handling in Consumer Behaviour, 1967, 1: 82-108.

[18]Slovic P. Perception of risk [J]. Science, 1987, 236(4799): 280-285.

[19]Borowsky A, Oron-Gilad T. Exploring the effects of driving experience on hazard awareness and risk perception via real-time hazard identification, hazard classification, and rating tasks [J]. Accident Analysis & Prevention, 2013, 59: 548-565.

[20]Xie C, Zhang J, Morrison A M. Developing a scale to measure tourist perceived safety [J]. Journal of Travel Research, 2020, 60(6): 1232-1251.

[21]陈农田, 宁威峰, 齐少桓. 民航飞行员进近着陆风险感知影响因素及能力测度 [J]. 人类工效学, 2022, 28(1): 43-47.

[22]李杰. 基于危险感知特性的施工作业人员事故倾向性评价方法研究 [D]. 镇江: 江苏大学, 2022.

[23]Li H, Chen H, Zhao Z, et al. Tunnel construction workers’ cognitive biases and unsafe behaviors: The mediating effects of risk perceptions [J]. Advances in Civil Engineering, 2020, 2020(1): 8873113.

[24]李广利. 煤矿员工风险感知量表开发与应用 [J]. 中国安全生产科学技术, 2022, 18(7): 128-134.

[25]李乃文, 苏子怡, 房小凯. 工作欠载对煤矿监检人员风险感知能力的影响研究 [J]. 中国安全科学学报, 2024, 34(2): 31-36.

[26]张羽, 赵碧柳, 刘红勇. 基于情绪分析的事故风险感知偏差研究 [J]. 中国安全科学学报, 2022, 32(8): 16-22.

[27]王春迪. 矿工风险感知影响机理研究 [D]. 阜新: 辽宁工程技术大学, 2018.

[28]李乃文, 房小凯. 睡眠剥夺对矿工风险感知的影响研究：基于生理试验 [J]. 安全与环境学报, 2024, 24(3): 1036-1043.

[29]张琦. 风险倾向对新生代建筑工人危险感知的影响机理研究 [D]. 徐州: 中国矿业大学, 2023.

[30]赵岑, 王晓峰, 黄先超. 有限理性视角下游客暴雨灾害风险感知评价——以南宫山景区为例 [J]. 地域研究与开发, 2018, 37(1): 120-124+137.

[31]王兴华, 胡林, 彭勇, 等. 汽车-动力两轮车碰撞前驾驶员风险感知研究 [J]. 汽车工程, 2024, 46(3): 498-508.

[32]秦雅琴, 张红强, 熊坚, 等. 风险驾驶模拟情境下驾驶人风险感知研究 [J]. 交通运输系统工程与信息, 2015, 15(2): 142-148.

[33]梁璐. 山区公路行人横穿下驾驶人风险感知研究 [D]. 昆明: 昆明理工大学, 2021.

[34]康建博. 工矿城市社区空间老化与人口老龄化的相关性研究 [D]. 徐州: 中国矿业大学, 2016.

[35]卢耀勤. 乌鲁木齐厂矿企业职业卫生与健康状况调查及对策研究 [D]. 乌鲁木齐: 新疆医科大学, 2020.

[36]田水承, 寇猛. 不同年龄组矿工安全行为能力的差异研究 [J]. 中国安全生产科学技术, 2015, 11(10): 179-184.

[37]田水承, 陈盈, 邹元. 手工搬运作业下矿工腰部肌肉疲劳分析 [J]. 矿业安全与环保, 2021, 48(5): 122-126.

[38]李广利, 温情, 苏锴轩, 等. 矿工不安全情绪对其风险偏好的影响研究 [J]. 安全与环境学报, 2024, 24(8): 3154-3164.

[39]陈冬博. 煤矿矿工敬业度与不安全行为关系研究 [D]. 太原: 太原理工大学, 2015.

[40]张景钢, 王清焱, 何鑫. 矿井智能通风现状与智能控制系统构建 [J]. 矿业安全与环保, 2023, 50(5): 37-42.

[41]Zuckerman M. Sensation seeking: Beyond the optimallevel of arousal [M]. Hillsdale: Lawrence Erlbaum Associates, 1979.

[42]Bexton W H, Heron W, Scott R H. Effects of decreased variation in the sensory environment [J]. Canadian Journal of Psychology, 1954, 8(2): 70-76.

[43]Kolin E A, Price L, Zoob I. Development of a sensation-seeking scale [J]. Journal of Consulting Psychology, 1964, 28(6): 477-482.

[44]Harden K P, Tucker-Drob E M. Individual differences in the development of sensation seeking and impulsivity during adolescence: Further evidence for a dual systems model [J]. Developmental Psychology, 2011, 47(3): 739-746.

[45]Arnett J J. Emerging adulthood. A theory of development from the late teens through the twenties [J]. American Psychologist, 2000, 55(5): 469-480.

[46]Chen C F. Personality, safety attitudes and risky driving behaviors-evidence from young Taiwanese motorcyclists [J]. Accident Analysis & Prevention, 2009, 41(5): 963-968.

[47]胡春梅, 李方珍, 余殊伶. 中等职业学校学生吸烟、饮酒、赌博与冲动性、感觉寻求的关系 [J]. 现代预防医学, 2017, 44(20): 3753-3756.

[48]丁凡栖. 急性酒精对感觉寻求者抑制控制的影响 [D]. 南昌: 江西师范大学, 2024.

[49]李欣忆, 杨玲. 戒毒人员负性情绪、感觉寻求、风险认知与复吸倾向的关系探析 [J]. 中国药物依赖性杂志, 2013, 22(4): 300-303.

[50]胡春梅, 尚春雅. 重庆市自报告有性行为青少年高危性行为发生情况与影响因素分析 [J]. 中国艾滋病性病, 2024, 30(8): 842-846.

[51]石岩, 郭显德, 钱民辉, 等. 用SSS量表对运动员感觉寻求人格特质的测量与分析 [J]. 心理科学, 1994, (4): 249-251.

[52]李金鑫. 感觉寻求对青少年冒险行为的影响：一个有调节的中介作用 [D]. 兰州: 西北师范大学, 2023.

[53]张雨青, 陈仲庚. 感觉寻求量表对我国被试的测定及因素分析 [J]. 心理科学通讯, 1990, (4): 3-8+66.

[54]陈丽娜, 张明, 金志成, 等. 中小学生感觉寻求量表的编制与应用 [J]. 心理发展与教育, 2006, (4): 103-108.

[55]赵闪. 大学生感觉寻求及其与心理健康关系的研究 [D]. 长春: 东北师范大学, 2005.

[56]Miyazaki A, Yano N, Suzuki Y. Sensation seeking, hazard perception, and driving attitude in older drivers [D]. Miyagi: Tohoku University, 2013.

[57]胡春梅, 何玲玲, 葛宁. 青少年感觉寻求对攻击行为影响：一个有调节的中介模型 [J]. 教育科学研究, 2021, (12): 20-26+46.

[58]张纬武, 胡春梅, 李方珍. 家庭因素对我国中职生非自杀性自我伤害行为的影响研究——基于感觉寻求和冲动性的中介效应分析 [J]. 教育科学研究, 2024, (3): 51-59.

[59]王梦琪. 大学生感觉寻求、跨文化接触与文化智力的关系研究 [D]. 武汉: 华中农业大学, 2023.

[60]蒋俊杰. 老年人微信过度使用与其心理安全感的关系 [D]. 重庆: 西南大学, 2023.

[61]Verma A K, Chivukula U. The effect of an affect, sensation seeking, and premeditation on risky decision-making: Conditional process analysis [J]. Plos One, 2023, 18(2): e0281324.

[62]Wang N, Chang R, Wu F. An analysis of the adolescents' hazard perception when crossing road from the perspective of personality characteristics based on an eye-tracking study [J]. Plos One, 2022, 17(5): e0267309.

[63]王健霖. 不同路况下感觉寻求对驾驶员风险感知的影响 [D]. 大连: 辽宁师范大学, 2018.

[64]林芜. 建筑安全员感觉寻求特质对施工场景风险感知的影响：眼动的证据 [D]. 漳州: 闽南师范大学, 2023.

[65]田水承, 王清焱, 李红霞, 等. 神经安全管理学理论基础及系统模型构建 [J]. 西安科技大学学报, 2025, 45(1): 26-34.

[66]Endsley M R. Measurement of situation awareness in dynamic systems [J]. Human Factors, 1995, 37(1): 65-84.

[67]Endsley M R. Toward a theory of situation awareness in dynamic systems [J]. Human Factors, 1995, 37(1): 32-64.

[68]Wickens C D, Helton W S, Hollands J G, et al. Engineering psychology and human performance [M]. New York: Routledge, 2021.

[69]Loukas M, Pennell C, Groat C, et al. Korbinian Brodmann (1868-1918) and his contributions to mapping the cerebral cortex [J]. Neurosurgery, 2011, 68(1): 6-11.

[70]Carlen M. What constitutes the prefrontal cortex? [J]. Science, 2017, 358(6362): 478-482.

[71]Ma Q, Jin J, Wang L. The neural process of hazard perception and evaluation for warning signal words: evidence from event-related potentials [J]. Neuroscience Letters, 2010, 483(3): 206-210.

[72]李元龙. 煤矿工人心理韧性、安全态度与安全行为的影响关系研究 [D]. 北京: 中国地质大学(北京), 2020.

[73]Gahan P, Harbridge R, Healy J, et al. The ageing workforce: Policy dilemmas and choices [J]. Australian Journal of Public Administration, 2016, 76(4): 511-523.

[74]Hashiguchi N, Sengoku S, Kubota Y, et al. Age-dependent influence of intrinsic and extrinsic motivations on construction worker performance [J]. International Journal of Environmental Research and Public Health, 2020, 18(1): 111.

[75]苏志，夏昭林. 工人老龄化与工作能力 [J]. 化工劳动卫生通讯, 1994, (4): 9-23.

[76]唐于红. 工作中成功老龄化：结构维度、影响因素及作用机制 [D]. 武汉: 中南财经政法大学, 2022.

[77]Zuckerman M. The psychophysiology of sensation seeking [J]. Journal of Personality, 1990, 58(1): 313-345.

[78]Zuckerman M. Behavioral expressions and biosocial bases of sensation seeking [M]. New York: Cambridge Press, 1994.

[79]Zuckerman M. Sensation seeking and risky behavior [M]. Washington: American Psychological Association, 2007.

[80]Delaney R, Strough J, Parker A M, et al. Variations in decision-making profiles by age and gender: A cluster-analytic approach [J]. Personality and Individual Differences, 2015, 85: 19-24.

[81]Bogon J, Jagorska C, Steinecker I, et al. Age-related changes in time perception: Effects of immersive virtual reality and spatial location of stimuli [J]. Acta Psychologica, 2024, 249: 104460.

[82]Chen Y, Ma X, Pethtel O. Age differences in trade-off decisions: Older adults prefer choice deferral [J]. Psychology and Aging, 2011, 26(2): 269-273.

[83]Steinberg L, Albert D, Cauffman E, et al. Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: Evidence for a dual systems model [J]. Developmental Psychology, 2008, 44(6): 1764-1778.

[84]Zuckerman M, Glicksohn J. Hans Eysenck's personality model and the constructs of sensation seeking and impulsivity [J]. Personality and Individual Differences, 2016, 103: 48-52.

[85]Naseer N, Hong K S. fNIRS-based brain-computer interfaces: A review [J]. Frontiers in Human Neuroscience, 2015, 9: 3.

[86]孙龙, 杨倩盈, 谷晨晨. 危险类型和感觉寻求对年轻新手驾驶员危险知觉的影响 [J]. 心理研究, 2020, 13(6): 538-543.

[87]栗继祖, 林健. 矿工感觉寻求倾向对冒险行为的影响 [J]. 现代职业安全, 2020, (1): 94-95.

[88]程磊, 孙洁. 2016—2022年我国煤矿事故统计与规律分析 [J]. 煤炭工程, 2023, 55(11): 125-129.

[89]孙林辉, 尚康, 袁晓芳. 基于LEC法的煤矿掘进作业岗位安全风险评价研究 [J]. 煤矿安全, 2019, 50(12): 248-252.

[90]张景钢, 王清焱, 赵淑枫. 基于贝叶斯网络的HAZOP-LOPA煤矿安全风险评价方法应用研究 [J]. 矿业安全与环保, 2022, 49(1): 114-120.

[91]Ehni H J, Wiesing U. The declaration of Helsinki in bioethics literature since the last revision in 2013 [J]. Bioethics, 2024, 38(4): 335-343.

[92]Cohen J. Statistical power aanalysis for the behavioral sciences [M]. New York: Routledge, 1988.

[93]Xiao X, Yu X, Zhang Z, et al. Transcranial brain atlas [J]. Science Advances, 2018, 4(9): eaar6904.

[94]Hou X, Zhang Z, Zhao C, et al. NIRS-KIT: A MATLAB toolbox for both resting-state and task fNIRS data analysis [J]. Neurophotonics, 2021, 8(1): 010802.

[95]Delpy D T, Cope M, Van Der Zee P, et al. Estimation of optical pathlength through tissue from direct time of flight measurement [J]. Physics in Medicine & Biology, 1988, 33(12): 1433-1442.

[96]Fishburn F A, Ludlum R S, Vaidya C J, et al. Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS [J]. NeuroImage, 2019, 184: 171-179.

[97]Guerrero-Mosquera C, Borragan G, Peigneux P. Automatic detection of noisy channels in fNIRS signal based on correlation analysis [J]. Journal of Neuroscience Methods, 2016, 271: 128-138.

[98]Wu R T. The present situation and related research on sensation seeking and anxiety, risk-taking behavior of college students [J]. Advances in Social Sciences, 2024, 13(3): 493-505.

[99]Fathlistya W, Mustika M D. How perceived individual safety attitude helps to explain the relationship between sensation seeking and risk-taking propensity in the prediction of individual work performance [J]. Jurnal RAP (Riset Aktual Psikologi Universitas Negeri Padang), 2021, 11(2): 191.

[100]成连华, 陈凯强, 赵国强, 等. 基于贡献率法的煤矿安全生产标准化绩效评估 [J]. 矿业安全与环保, 2023, 50(5): 156-162.

[101]Tian F, Qiu W, Li H, et al. Understanding miners' risk decisions: A multimodal analysis of personality, job burnout, and behavioral responses [J]. Job Burnout, and Behavioral Responses, 2025, 15: 1243.

[102]Keeney M J, Wiggins S, Reynolds K D, et al. Cognitive task analysis of miner preparedness to self-escape from mine emergencies [J]. Journal of Organizational Psychology, 2018, 18(4): 57-78.

[103]Lerner J, Carnevale J J, Inbar Y. Individual differences in need for cognition and decision-making competence among leaders [J]. Personality and Individual Differences, 2010, 51(3): 274-278.

[104]Magar E, Phillips L, Hosie J. Self-regulation and risk-taking [J]. Personality and Individual Differences, 2008, 45(2): 153-159.

[105]Miller B M, Sinclair J. Risk perceptions in a resource community and communication implications: Emotion, stigma, and identity [J]. Risk Analysis, 2012, 32(3): 483-495.

[106]Wang J, Liu-Lastres B, Ritchie B, et al. Risk reduction and adventure tourism safety: An extension of the risk perception attitude framework (RPAF) [J]. Tourism Management, 2019, 74: 247-257.

[107]Wan Z, Rolls E T, Cheng W, et al. Sensation-seeking is related to functional connectivities of the medial orbitofrontal cortex with the anterior cingulate cortex [J]. NeuroImage, 2020, 215: 116845.

[108]Wiklund J, Yu W, Tucker R, et al. ADHD, impulsivity and entrepreneurship [J]. Journal of Business Venturing, 2017, 32(6): 627-656.

[109]Cope L M, Martz M E, Hardee J E, et al. Reward activation in childhood predicts adolescent substance use initiation in a high-risk sample [J]. Drug Alcohol Depend, 2019, 194: 318-325.

[110]De Oliveira L, Portugal L C L, Pereira M, et al. Predicting bipolar disorder risk factors in distressed young adults from patterns of brain activation to reward: A machine learning approach [J]. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2019, 4(8): 726-733.

[111]Baltruschat S, Candido A, Megias A, et al. Risk proneness modulates the impact of impulsivity on brain functional connectivity [J]. Human Brain Mapping, 2020, 41(4): 943-951.

[112]Blankenstein N E, Schreuders E, Peper J S, et al. Individual differences in risk-taking tendencies modulate the neural processing of risky and ambiguous decision-making in adolescence [J]. NeuroImage, 2018, 172: 663-673.

[113]Qu W, Zhang W, Ge Y. The moderating effect of delay discounting between sensation seeking and risky driving behavior [J]. Safety Science, 2020, 123: 104558.

[114]Lecun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436-444.

[115]Mulumba D M, Liu J, Hao J, et al. Application of an optimized PSO-BP neural network to the assessment and prediction of underground coal mine safety risk factors [J]. Applied Sciences, 2023, 13(9): 5317.

[116]Kennedy J, Eberhart R. Particle swarm optimization [J]. Proceedings of ICNN'95 - International Conference on Neural Networks, 2002, 4: 1942-1948.

[117]丁宇. 基于脑电与心电融合的矿工疲劳状态识别研究 [D]. 西安: 西安建筑科技大学, 2023.