Research on impact of different illuminances on miners' attention based on EEG signals

doi:10.16265/j.cnki.issn1003-3033.2025.09.1223

Abstract

Abstract:

To explore the impact of underground lighting on miners' cognition, an EEG experiment was designed under different lighting conditions (100, 60, 30, 15 lx). EEG signals features were extracted using power spectral density, and α, β and θ rhythmic waves were selected for analyzing attention feature values. The analysis results were validated by using the Schultz grid test. The results indicate that β waves always occupy a larger proportion in the energy proportion graph. In the brain map, it reaches the maximum value in working condition 2, indicating that the subject is focused. The attention eigenvalue (β/θ value) shows a downward trend in the four working conditions. The β/θ value of working condition 4 decreases by 54.86% compared with that of working condition 2 at 55 min of the test, which indicates that the miner's attention is optimal at the light intensity of 60 lx, and significantly weakens at 15 lx. The Schultz grid test has the shortest duration and lowest error rate in condition 2, and the reliability reaches 84.83%, while under condition 4, the reliability is only 73.15%. The number of errors made by miners significantly increases, and attention shows a downward trend.

Key words: electroencephalogram (EEG), illumination, miners' attention, rhythm waves, reaction time

CLC Number:

X912安全生理学

JIANG Song, LIU Dizhen, CHEN Ying, JIANG Guowei, XU Zhonghua, HE Runfeng. Research on impact of different illuminances on miners' attention based on EEG signals[J]. China Safety Science Journal, 2025, 35(9): 113-120.

Figures/Tables 12

Table 1

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 2

Fig.6

Fig.7

Fig.8

Table 3

Fig.9

References 23

[1]	张舒, 王双, 史秀志. 不安全行为决策中情绪作用机制的脑电研究[J]. 中国安全科学学报, 2023, 33(1): 32-40.
	ZHANG Shu, WANG Shuang, SHI Xiuzhi. An EEG study of emotion mechanism in unsafe behavior decision-making[J]. China Safety Science Journal, 2023, 33(1):32-40. doi: 10.16265/j.cnki.issn1003-3033.2023.01.2435
[2]	李红霞, 徐浩冉, 田水承. 基于随机森林的矿工不安全行为预测预警模型[J]. 中国安全科学学报, 2022, 32(12): 10-18. doi: 10.16265/j.cnki.issn1003-3033.2022.12.2752
	LI Hongxia, XU Haoran, TIAN Shuicheng. A prediction and early warning model of miners' unsafe behavior based on random forest[J]. China Safety Science Journal, 2022, 32(12):10-18. doi: 10.16265/j.cnki.issn1003-3033.2022.12.2752
[3]	景国勋, 田国良, 蒋方. 2016—2022年我国煤矿顶板事故统计分析及防治对策[J]. 煤炭工程, 2023, 55(10): 74-79.
	JING Guoxun, TIAN Guoliang, JIANG Fang. Statistical analysis and countermeasures of coal mine roof accidents from 2016 to 2022[J]. Coal Engineering, 2023, 55(10):74-79.
[4]	马申, 胡松涛, 辛淞, 等. 基于因子分析的人体疲劳及光舒适综合评价研究[J]. 青岛理工大学学报, 2019, 40(2): 101-106.
	MA Shen, HU Songtao, XIN Song, et al. Comprehensive evaluation of human fatigue and light comfort based on factor analysis[J]. Journal of Qingdao University of Technology, 2019, 40(2):101-106.
[5]	贾爱芳, 田水承, 郭昕玥, 等. 矿工心理疲劳试验研究[J]. 安全与环境学报, 2021, 21(6): 2608-2616.
	JIA Aifang, TIAN Shuicheng, GUO Xinyue, et al. Experimental study on mental fatigue of miners[J]. Journal of Safety and Environment, 2021, 21(6):2608-2616.
[6]	范庆月. 基于多生理信号的VDT作业照明环境舒适性研究[D]. 沈阳: 沈阳工业大学, 2022.
	FAN Qingyue. Study on lighting environment comfot of VDT work based on multiple physiological signals[D]. Shenyang: Shenyang University of Technology, 2022.
[7]	鹿铭理. 基于脑电的光环境评价方法及实验研究[D]. 青岛: 青岛理工大学, 2021.
	LU Mingli. Evaluation method and experimental research of light environment based on EEG[D]. Qingdao: Qingdao University of Technology, 2021.
[8]	LI Jing, WANG Zhen, QIN Yaru, et al. Study on the influence of an underground low-light environment on human safety behavior[J]. International Journal of Occupational Safety and Ergonomics: JOSE, 2022, 28(1): 305-314.
[9]	邹亚双. 井工煤矿环境照度和噪声强度对视疲劳的影响研究[D]. 徐州: 中国矿业大学, 2023.
	ZOU Yashuang. Study on the influence of ambient illumination and noise intensity on visual fatiguein underground coal mines[D]. Xuzhou: China University of Mining and Technology, 2023.
[10]	LI Jing, QIN Yaru, GHENG Cuan, et al. Lighting for work: a study on the effect of underground low-light environment on miners' physiology[J]. Environmental Science and Pollution Research, 2022, 29(8): 11 644-11 653.
[11]	卢才武, 徐晓慧, 高睿阳, 等. 热湿环境下矿工注意力对应急决策影响的脑电研究[J]. 安全与环境学报, 2023, 23(10): 3641-3647.
	LU Caiwu, XU Xiaohui, GAO Ruiyang, et al. EEG research on the emergency decision in thermal and humidity environment[J]. Journal of Safety and Environment, 2023, 23(10):3641-3647.
[12]	GOLJAHANI A, BISIACCHI P, SPARACINO G. An EEGLAB plugin to analyze individual EEG alpha rhythms using the "channel reactivity-based method"[J]. Computer Methods and Programs in Biomedicine, 2014, 113(3): 853-861. doi: 10.1016/j.cmpb.2013.12.010 pmid: 24439522
[13]	DELORME A, MAKEIG S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J]. Journal of Neuroscience Methods, 2004, 134(1): 9-21. doi: 10.1016/j.jneumeth.2003.10.009 pmid: 15102499
[14]	卢才武, 高睿阳, 徐晓慧, 等. 基于脑电数据的不同噪声工况下矿工注意力研究[J]. 中国安全科学学报, 2023, 33(5): 35-41. doi: 10.16265/j.cnki.issn1003-3033.2023.05.2228
	LU Caiwu, GAO Ruiyang, XU Xiaohui, et al. Study on miners' attention under different noise conditions based on EEG data[J]. China Safety Science Journal, 2023, 33(5):35-41. doi: 10.16265/j.cnki.issn1003-3033.2023.05.2228
[15]	PIRONDINI E, COSCIA M, MINGUILLON J, et al. EEG topographies provide subject-specific correlates of motor control[J]. Scientific Reports, 2017, 7(1): DOI:10.1038/s41598-017-13482-1.
[16]	赵德春, 沈利豪, 蒋宇皓, 等. 基于共空间模式和功率谱密度的脑电信号分类[J]. 科学技术与工程, 2023, 23(10): 4272-4278.
	ZHAO Dechun, SHEN Lihao, JIANG Yuhao, et al. Classification of electroencephalogram signals based on common space pattern and power spectrum density[J]. Science Technology and Engineering, 2023, 23(10):4272-4278.
[17]	张洁, 庞丽萍, 完颜笑如, 等. 基于脑电功率谱密度的作业人员脑力负荷评估方法[J]. 航空学报, 2020, 41(10): 118-125.
	ZHANG Jie, PANG Liping, WANYAN Xiaoru, et al. Method for operator mental workload assessment based on power spectral density of EEG[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10):118-125.
[18]	AZAMI H, ZRENNER C, BROOKS H, et al. Beta to theta power ratio in EEG periodic components as a potential biomarker in mild cognitive impairment and Alzheimer's dementia[J]. Alzheimer's Research & Therapy, 2023, 15(1): DOI:10.1186/s13195-023-01280-z.
[19]	杨文阳, 张文瑄. 基于脑电信号的注意力水平评价研究进展[J]. 生物医学工程学杂志, 2023, 40(4): 820-828. pmid: 37666775
	YANG Wenyang, ZHANG Wenxuan. Research progress on attention level evaluation based on electroencephalogram signals[J]. Journal of Biomedical Engineering, 2023, 40(4):820-828. doi: 10.7507/1001-5515.202208085 pmid: 37666775
[20]	LALO E, GILBERTSON T, DOYLE L, et al. Phasic increases in cortical beta activity are associated with alterations in sensory processing in the human[J]. Experimental Brain Research, 2007, 177(1): 137-145. doi: 10.1007/s00221-006-0655-8 pmid: 16972074
[21]	NAN Wenya, WAN Feng, VAI Mang I, et al. Resting and initial beta amplitudes predict learning ability in beta/theta ratio neurofeedback training in healthy young adults[J]. Frontiers in Human Neuroscience, 2015,9: DOI:10.3389/fnhum.2015.00677.
[22]	YANG Limin, NAN Wenya, QU Xiaoting, et al. Beta/theta ratio neurofeedback training effects on the spectral topography of EEG[C]. 2015 37^th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan: IEEE, 2015: 4741-4744.
[23]	景国勋, 吕鹏飞, 周霏, 等. 基于E-Prime的煤矿噪声对矿工反应时与出错数的影响效应研究[J]. 安全与环境学报, 2018, 18(6): 2285-2289.
	JING Guoxun, LYU Pengfei, ZHOU Fei, et al. Impact of the coal-mining noise on the miners' reaction and their error-making frequency based on E-Prime[J]. Journal of Safety and Environment, 2018, 18(6):2285-2289.

照明地点	最低照度/lx
一般电气设备硐室及其他硐室	50
爆破器材库	30
信号室、调度室	75
机械化运输巷道、候车室	20
井底车场及其附近巷道	15
保健站	100

节律波	w/Hz	人体生理反应
δ	0.5~4	深度睡眠状态,为“无意识”波
θ	4~8	试图抑制困意或有抑郁时,为“潜意识”波
α	8~13	呈放松、闭眼等清醒状态,为“健康”波
β	13~30	专注、高度警觉的状态下

舒尔特方格	任务分组	工况1	工况2	工况3	工况4
反应时间/s	A	21.63±3.86	23.36±2.66	23.88±2.04	26.79±2.21
反应时间/s	B	23.66±4.39	21.15±2.48	25.08±3.11	30.04±2.66
出错次数	A	4.43±1.38	2.11±1.25	3.43±2.80	4.96±1.12
出错次数	B	3.14±2.43	3.96±0.88	4.58±1.91	5.78±3.31
平均可靠度/%	—	81.08	84.83	79.98	73.15