煤矿事故中由于煤矿工人因警觉度水平降低导致不安全行为和操作失误的案例屡见不鲜，以往的研究发现情绪状态的不稳定更易导致矿工注意力等能力的下降，所以研究煤矿工人不同情绪状态对警觉度的影响有重要意义。脑电信号（EEG）在不同的警觉水平下表现为不同频段，而各个频段信号对应着不同的生理学意义，这为研究不同情绪状态对矿工警觉度变化产生的影响差异提供了新的方法和思路。近年来关于矿工警觉度、矿工情绪的研究也初步取得了一些成果，但情绪对于警觉度的影响程度和调节效果却鲜有研究。本研究使用脑电信号采集系统结合主观量表和PVT任务实验三种方法研究不同情绪状态对矿工警觉度的影响差异以及不同情绪状态在调控警觉度水平方面的区别。

本文首先借助Citespace软件对关于情绪和警觉水平对于矿工影响的研究现状进行关键词和聚类分析，对研究进展和相关理论基础进行梳理阐述，为后续的实验设计和实验结果的分析确立了基础依据，提供了科学方法。然后，通过设计情绪诱发材料有效性检测实验和基于脑电信号的PVT实验，让被试填写主观量表、借助SPSS统计分析软件验证情绪诱发效果、使用脑电信号采集系统和E-prime软件采集并记录被试在不同情绪诱发条件下实验中的主观量表数据、PVT任务数据和脑电数据，并对采集到的脑电数据做初步处理，为后续的研究分析收集指标。最后对实验中的数据结果进行分析，分别从时间阶段、情绪类型、情绪强度、实验时间、情绪和实验阶段的交互作用等维度通过双因素重复测量方差分析、简单效应检验等统计方法分析比较自评量表和行为数据结果，借助MATLAB对预处理后的脑电信号进行分段，绘制相对功率地形图，对比分析各频段的脑电数据，并对各通道的功率谱密度进行配对样本T检验，比较不同情绪对警觉度影响的差异、不同情绪在对警觉度调节作用上的差异，以及不同情绪对警觉度的影响在各频段的通道上表现出的特征规律。研究结果初步证明了情绪的不同类型和不同强度在影响警觉度上存在明显差异，负性情绪比正性情绪更易影响警觉度使其警觉水平降低；同种类型不同程度的情绪中，含“感兴趣的、精神活力高的、劲头足的”等积极因子的情绪在维持调节警觉度上表现出了比包含“热情的、意志坚定的”积极因子的情绪更好的效果，含“心神不宁的、恐惧的、坐立不安的”的负性因子的情绪比含“心烦的、敌意的、易怒的”负性因子的情绪更能加剧警觉度水平的下降。通过EEG的频段对比以及通道的配对样本T检验发现，多数通道具有明显差异，各频段通道无明显差异的大多表现FP1、FPZ、FP2、AF3、AF4这5个通道，正性情绪和负性情绪不同程度的组内比较发现差异最显著的区域在枕区。

本研究验证了矿工不同情绪状态对警觉度的影响和差异，进一步从脑活动特征揭示了情绪对警觉度的影响程度及规律特点。同时为后续基于可穿戴的脑电设备通道选取上提供了新思路。

In coal mine accidents, there are many cases of unsafe behavior and operational errors caused by the reduction of coal mine workers' alertness. Previous studies have found that the instability of emotional state is more likely to lead to the decline of miners' attention and other abilities. Therefore, it is of great significance to study the impact of different emotional states of coal mine workers on alertness. Electroencephalogram (EEG) signals show different frequency bands under different alert levels, and the corresponding signals of each frequency band have different physiological significance, which provides a new method and idea for studying the influence of different emotional states on the change of miners' alertness. In recent years, the research on miners' alertness and miners' emotion has also made some preliminary achievements, but there is little research on the regulation and influence of emotion on miners' alertness. In this study, EEG acquisition system, subjective scale and PVT task experiment were used to study the influence of different emotional states on miners' alertness and the difference of different emotional states in regulating alertness level.

Firstly, with the help of Citespace software, this paper makes keyword and cluster analysis on the research status of the impact of emotion and alertness on miners, combs and expounds the research progress and relevant theoretical basis, which establishes a scientific basis and lays a foundation for subsequent experimental design and data analysis. Then, by designing the effectiveness detection experiment of emotion inducing materials and the PVT experiment based on EEG signals, the subjects were asked to fill in the subjective scale, verify the emotion inducing effect with the help of SPSS statistical analysis software, collect and record the behavior data and EEG data of the subjects under different emotion inducing conditions by using EEG signal acquisition system and prime software, and preprocess the EEG data to collect analysis indicators for subsequent analysis and research. Finally, the data results in the experiment are analyzed. From the dimensions of time stage, emotion type, emotion intensity, experimental time, emotion and the interaction of experimental stage, the results of self-assessment scale and behavior data are analyzed and compared through statistical methods such as two factor repeated measurement analysis of variance and simple effect test. The preprocessed EEG signals are segmented with the help of MATLAB to draw the relative power topographic map, The EEG data of each frequency band are compared and analyzed, and the paired sample t-test is carried out for the power spectral density of each channel. The differences of the effects of different emotions on alertness, the differences of the regulatory effects of different emotions on alertness, and the characteristic laws of the effects of different emotions on alertness in the channels of each frequency band are compared. The results show that different types and intensities of emotions have significant differences in the degree of alertness. Negative emotions are more likely to affect the degree of alertness and reduce the level of alertness than positive emotions; Among the emotions of the same type with different degrees, those with positive factors such as "interested, energetic and energetic" have a better effect on maintaining regulatory vigilance than those with "enthusiastic and firm will", Emotions with "restless, fearful and restless" negative factors can exacerbate the decline of vigilance level more than those with "upset, hostile and irritable" negative factors. Through the comparison of EEG frequency bands and the paired sample t-test of channels, it is found that most of the channels with no significant difference in each frequency band are FP1,FPZ,FP2,AF3 and AF4.In the group comparison of different degrees of positive emotion and negative emotion, it is found that the most significant difference is in the occipital region.

This study verified the influence and difference of miners' different emotional states on alertness, and further revealed the influence degree and regular characteristics of emotion on alertness from the characteristics of brain activity. At the same time, it provides a new idea for the channel selection of wearable EEG devices.

[1]国家统计局.中华人民共和国2020年国民经济和社会发展统计公报[EB/OL].http：//www.stats.gov.cn/ztjc/zthd/lhfw/2021/lh_hgjj/202103/t20210301_1814216.html.

[2]宋建萍.煤矿事故致因模式分析及应用研究[J].集宁师范学院学报,2020,42(05):113-116.

[3]张涵,王峰.基于矿工不安全行为的煤矿生产事故分析及对策[J].煤炭工程,2019,51(08):177-180.

[4]唐健.情绪与安全研究[J].电力安全技术,2013,15(07):8-10.

[5]吕威建,吕永卫.角色压力对矿工不安全行为的影响研究[J].煤矿安全,2021,52(04):255-259.

[6]杨雪,冯念青,张瀚元,等.情感事件视角矿工不安全行为影响因素SD仿真[J].煤矿安全,2020,51(03):252-256.

[7]栗继祖,那赟.远离负性情绪稳定安全生产[J].现代职业安全,2019(05):89-91.

[8]任大伟,徐小华,梁亚敏,等.煤矿井下作业疲劳影响因素及对策研究[J].煤矿安全,2019,50(03):231-234.

[9]代丽芳.煤炭企业一线员工消极情绪来源及对策研究[J].农家参谋,2020(09):195-225.

[10]亢高洁,冀巨海.煤矿工人社会资本对安全行为的影响机制研究[J].煤矿安全,2020,51(04):253-256.

[11]李乃文,郭利霞.基于Multi-agent的矿工情绪稳定性模型构建[J].中国安全生产科学技术,2014,10(12):172-177.

[12]傅佳伟,石立臣,吕宝粮.基于EEG的警觉度分析与估计研究综述[J].中国生物医学工程学报,2009,28(04):589-596.

[13]周鹏,张翼,李向新,等.大脑警觉度检测的研究现状与进展[J].生物医学工程学杂志,2012,29(03):574-578.

[14]王璟.矿工安全心理资本与不安全行为的关系研究[D].西安科技大学,2016.

[15]邸鸿喜.煤矿工人工作压力结构、传播规律及其对不安全行为的影响研究[D].西安科技大学,2017.

[16]邓慧君.基于结构方程模型的新疆煤矿工人职业倦怠影响因素研究[D].新疆医科大学,2021.

[17]杨锦竺,罗永秀.矿工心理重压亟待关注[J].当代矿工,2006(02):43-44.

[18]吴鹏.煤矿组织霸凌与员工行为选择关系研究:心理调焦的影响[D].中国矿业大学,2018.

[19]刘涛杰,张凯,王明君,等.理情行为疗法在工伤矿工情绪障碍中的应用研究:第八届北京国际康复论坛,中国北京,2013[C].

[20]尹贻勤.矿工作业意外差错心理原因分析与预防对策[J].华北科技学院学报,2008(03):54-58.

[21]景树伟.组织认同、主观幸福感对矿工不安全行为的影响研究[D].太原理工大学,2020.

[22]李洪波.矿难后矿工创伤后应激障碍的研究[D].吉林大学,2011.

[23]贾慧悦,李燕,郭晗薇,等.矿工子女的人格特点及其与亲子沟通的关系[J].中国健康心理学杂志,2007(11):1038-1039.

[24]赵霞,孟祥浩,刘星期,等.女性特殊工种矿工心理测量与比较分析[J].中国健康心理学杂志,2012,20(10):1514-1516.

[25]茹晓云.煤矿矿工的不安全行为分析及预防控制措施[J].煤,2010,19(02):74-76.

[26]孙文献,毕庆平,傅绍华,马松,李建明.焦虑状态与矿工发生工伤相关研究[J].健康心理学,1995(03):19-20.

[27]代丽芳.矿工情绪管理研究[D].辽宁工程技术大学,2012.

[28]刘波.安全情感文化对矿工不安全情绪的影响研究[D].西安科技大学,2018.

[29]李乃文,冀永红,唐水清,等.高危岗位矿工情绪稳定性影响机制SD仿真[J].中国安全生产科学技术,2018,14(05):167-173.

[30]田水承,杨鹏飞,李磊,等.矿工不良情绪影响因素及干预对策研究[J].矿业安全与环保,2016,43(06):99-102.

[31]李广利,严一知,刘文琦,等.基于DEMATEL-ISM的矿工不安全情绪形成因子研究[J].中国安全科学学报,2021,31(07):30-37.

[32]仝凤鸣,杨雪,卢亚丽.基于多主体建模的煤矿员工不安全情绪演化分析[J].煤矿安全,2021,52(08):259-264.

[33]张良斐.恐慌情绪下矿工应急行为研究[D].辽宁工程技术大学,2014.

[34]李焕.情绪与矿工不安全行为关系实验研究[D].西安科技大学,2015.

[35]惠璐.不安全情绪对矿工风险决策影响的实验研究[D].西安科技大学,2018.

[36]沈剑,李红霞.矿工作业疲劳对煤矿险兆事件的影响机理——基于情感耗竭中介变量的分析[J].安全与环境学报,2019,19(02):527-534.

[37]Lu H,Wang M,Sangaiah A K. Human Emotion Recognition Using an EEG Cloud Computing Platform[J]. Mobile Networks and Applications,2018,25:1023-1032.

[38]杨雪,杨娟,张瀚元.基于系统动力学的矿工情绪演化机理研究[J].煤矿安全,2021,52(01):252-256.

[39]M. M M. EMOTION AND COAL MINERS[J]. JAMA: The Journal of the American Medical Association,1955,157(1).

[40]Mei W,Songzhi Z,Yuanjie L,et al. Anxiety Level Detection Using BCI of Miner’s Smart Helmet[J]. Mobile Networks and Applications,2018,23(2).

[41]Yonghui L,Jiahui Y,Meifen W,et al. A Comprehensive Model of the Relationship between Miners’ Work Commitment[J]. Cultural Emotion and Unemployment Risk Perception,2021,13(5).

[42]Yanyu G,Jizu L. The Effect of Emotional Intelligence on Unsafe Behavior of Miners: The Role of Emotional Labor Strategies and Perceived Organizational Support.[J]. International journal of occupational safety and ergonomics : JOSE,2022.

[43]Malmo R B,Surwillo W W. Sleep deprivation: Changes in performance and physiological indicants of activation.[J]. Psychological Monographs: General and Applied,1960,74(15).

[44]ÄKERSTEDT T,FOLKARD S. Predicting sleep latency from the three‐process model of alertness regulation[J]. Psychophysiology,2007,33(4).

[45]Mathias B,Daniel M,David F D. Validity and sensitivity of a brief psychomotor vigilance test (PVT-B) to total and partial sleep deprivation[J]. Acta Astronautica,2011,69(11).

[46]Michael E,Renate E,Helga A H,et al. On the need of objective vigilance monitoring: Effects of sleep loss on target detection and task-negative activity using combined EEG/fMRI[J]. Frontiers in Neurology,2012,3.

[47]Allen A E,Hazelhoff E M,Martial F P,et al. Exploiting Metamerism to Regulate the impact of a Visual Display on Alertness and Melatonin Suppression Independent of Visual Appearance.[J]. Sleep,2018,41(8).

[48]Fonseca A,Kerick S,King J,et al. Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data.[J]. Frontiers in human neuroscience,2018,12.

[49]Nowak K,Łukomska B. The impact of shift work on the wellbeing and subjective levels of alertness and sleepiness in firefighters and rescue service workers.[J]. International journal of occupational safety and ergonomics : JOSE,2021,27(4).

[50]VitalLopez F G,Doty T J,Reifman J. Optimal Sleep and Work Schedules to Maximize Alertness.[J]. Sleep,2021,44(11).

[51]Dugdale Z,Eiter B,Chaumont M C,et al. Findings from a systematic review of fatigue interventions: What's (not) being tested in mining and other industrial environments.[J]. American journal of industrial medicine,2022,65(4).

[52]Anne B,Nadège D,Pascale T,et al. Vigilance and intrinsic maintenance of alert state: An ERP study[J]. Behavioural Brain Research,2010,211(2).

[53]E. Aserinsky N H. Iivo Types of Ocular Motility Occurring in Sleep[J]. Journal of applied physiology,1955,8(1):1-10.

[54]于洪.基于脑电信号的警觉度估计[D].上海交通大学,2007.

[55]赵云龙,王学民,薛然婷,等.基于复杂性度量的大脑警觉度分析[J].生物医学工程学杂志,2015,32(04):725-729.

[56]罗明,郑诗琪,叶鸣.动态光对人警觉度与表现的影响[J].照明工程学报,2016,27(06):1-5.

[57]郑诗琪.光照对人体的警觉度、任务表现与生理节律的影响[D].浙江大学,2018.

[58]王涛.工作照明环境对人体警觉度的影响及调节作用研究[D].华侨大学,2020.

[59]张露.基于视觉通道闪光刺激的大脑警觉度调控技术研究[D].天津大学,2016.

[60]田云.主客观警觉度的睡眠剥夺易损性差异及其神经机制[D].西南大学,2020.

[61]周鹏,朱越,董晓,等.睡眠限制引起的生物节律紊乱对大脑警觉度影响的研究[J].航天医学与医学工程,2020,33(06):504-511.

[62]薛然婷,周鹏,高翔,等.睡眠剥夺下警觉度变化的非线性研究[J].生物医学工程学杂志,2014,31(03):506-510.

[63]董书琴,谢宏.基于CSP与SVM算法的警觉度脑电信号分类[J].微型机与应用,2011,30(16):82-84.

[64]石立臣.基于脑电信号的警觉度估计研究[D].上海交通大学,2012.

[65]吴天翔.基于视频人脸图像的警觉度估计研究[D].上海交通大学,2013.

[66]叶小凤.模拟驾驶环境下基于脑电信号的警觉度分析[D].东北大学,2017.

[67]车丹丹.矿工警觉度对不安全行为的影响研究[D].西安科技大学,2017.

[68]吴志敏,潘雨帆,洪治潮.基于模糊神经网络的驾驶警觉度识别方法研究[J].交通运输研究,2018,4(03):30-35.

[69]周鹏,王学民,朱险峰,等.基于多生理参数的大脑警觉度检测技术及其对抗措施研究[Z].2018.

[70]周鹏,朱越,董晓,等.睡眠限制引起的生物节律紊乱对大脑警觉度影响的研究[J].航天医学与医学工程,2020,33(06):504-511.

[71]姚迪.基于卷积神经网络的高铁司机脑电疲劳检测研究[D].西南交通大学,2020.

[72]AF M,SJ L,BP J. On familial factors in the attentional deficit in schizophrenia: A review and report of two new subject samples[J]. Journal of Psychiatric Research，1992，26(4):383-403.

[73]Cohen J D.Servan-Schreiber,Context,cortex,and dopamine: A connectionist approach to behavior and biology in schizophrenia[J]. Cognitive modeling,2002,99(1):377-421.

[74]Caggiano D M,Parasuraman R. The role of memory representation in the vigilance decrement.[J]. Psychonomic bulletin & review,2004,11(5).

[75]Robbins TW E B. Neurobehavioral mechanisms of reward and motivation[J]. Current Opinion Neurobiology,1996,6(2):228-236.

[76]McEwen B S. The neurobiology of stress: from serendipity to clinical relevance.[J]. Brain research,2000,886(1-2).

[77]Bonnet M H,Arand D L. The consequences of a week of insomnia.[J].Sleep,1996,19(6).

[78]Gerald P K. Sustained work,fatigue,sleep loss and performance: A review of the issues[J]. Taylor & Francis Group,2007,3(2).

[79]Dinges,F D. Microcomputer analyses of performance on a portable,simple visual RT task during sustained operations,behavior research methods[J]. Instruments and Computers,1985,17(6):652-655.

[80]Yamaguchi M,Deguchi M,Wakasugi J,et al. Hand-held monitor of sympathetic nervous system using salivary amylase activity and its validation by driver fatigue assessment.[J]. Biosensors & bioelectronics,2006,21(7).

[81]Al P L E. [Reliability and validity of the simplified Chinese version of Epworth sleepiness scale][J].Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery,2011,46(1):44-49.

[82]Shahid A W K M S. Stanford Sleepiness Scale (SSS)[J]. Stop That & One Hundred Other Sleep Scales,2011.

[83]C R. Rating of perceived exertion (RPE)[J]. Journal of Physiotherapy,2012,58(1):62.

[84]Subramaniam K,Krishnaswamy S,Jemain A A,et al. The Clinical Interview Schedule-Revised (CIS-R)-Malay Version,Clinical Validation.[J].The Malaysian journal of medical sciences : MJMS,2006,13(1).

[85]Anna Å M,Göran K,Torbjörn Å. Comparing two versions of the Karolinska Sleepiness Scale (KSS)[J]. Sleep and Biological Rhythms,2016,14(3).

[86]陈曾.脑电信号在情感识别中的研究[D].西南大学,2010.

[87]Cho-Yan Chen C W E C. Detecting sustained attention during cognitive work using heart rate variability,Intelligent Information Hiding and Multimedia Signal Processing(IIH-MSP): 2010 Sixth International Conference,2010[C].

[88]S M,M I. Lapse in alertness:coherence of fluctuations in performance and EEG spectrum[J]. Electroencephalography and Clinical Neurophysiology,1993,86(1):23-25.

[89]Performance decrement during prolonged night driving:Riemersma,J.B.J.et al In:Vigilance: Theory,Operational Performance,and Physiological Correlates,Edited by R.R. Mackie,Plenum Press,New York and London,1977,pp 41â 58[J]. Elsevier,1978,9(3).

[90]张钦.《普通心理学》[M].北京:中国人民大学出版社,2012.

[91]彭晓哲,周晓林.情绪信息与注意偏向[J].心理科学进展,2005,13(4):488-496.

[92]李颖.突发公共卫生事件中的情绪传播与舆论引导[J].山西师大学报(社会科学版),2021,48(02):39-45.

[93]Gupta S K A T S G. Students‘affective content analysis in smart classroom environment using deep learning techniques [J]. Multimedia Tools and Applications，2019，18(78)：25321-25348.

[94]张旺婷.基于LSTM和多任务神经网络的方面级别情感分析研究[D].北京交通大学,2020.

[95]张晶,朱波.微博文本的汉语情绪强度[J].华侨大学学报(哲学社会科学版),2020(06):146-153.

[96]刘晔.情绪材料诱发情绪方法研究综述[J].才智,2018(02):232.

[97]付玉.情绪诱发方法研究综述[J].湖北农机化,2019(16):43-45.

[98]Wanlu Y,Kai M,Takashi N,et al.Affective auditory stimulus database: An expanded version of the International Affective Digitized Sounds (IADS-E)[J]. Behavior Research Methods,2018,50(4).

[99]刘涛生,马慧,黄宇霞,等.建立情绪声音刺激库的初步研究[J].中国心理卫生杂志,2006(11):709-712.

[100]Lang P J B M M. International Affective Picture System (IAPS) : Technical Manual And Affective Ratings[M]. Gainesville: University of Florida: Gainesville: University of Florida,1999.

[101]白露,马慧,黄宇霞,等.中国情绪图片系统的编制:第十届全国心理学学术大会,中国上海,2005[C].

[102]王一牛,周立明,罗跃嘉.汉语情感词系统的初步编制及评定[J].中国心理卫生杂志,2008(08):608-612.

[103]James J G,Robert W L. Emotion elicitation using films[J]. Cognition & Emotion,1995,9(1).

[104]徐鹏飞,黄宇霞,罗跃嘉.中国情绪影像材料库的初步编制和评定[J].中国心理卫生杂志,2010,24(07):551-554.

[105]纪强.基于VR与EEG的情绪识别系统设计与实现[D].华南理工大学,2017.

[106]那赟,栗继祖.工作价值观、调节焦点与安全绩效的关系研究——基于煤矿企业新生代员工的实证分析[J].中国安全生产科学技术,2019,15(08):162-167.