Study on miners' attention under different noise conditions based on EEG data

doi:10.16265/j.cnki.issn1003-3033.2023.05.2228

Abstract

Abstract:

In order to explore the negative impact of noise on the brain cognition of miners, this study carried out an attention study under different noise conditions (30, 50, 70, 90 dB) by designing an EEG test and combining with the noise collected in Fuquan Mine, Shandong Province. Wavelet packet transform was used to extract the features of the collected EEG signals, and the value of θ/β was selected as the attention feature value, and the analysis results were further verified by Stroop test. The results show that β wave always occupies the dominant position in the energy proportion map, and reaches the peak at the second condition in the brain topography map. The characteristic value of attention (θ/β) shows an overall increasing trend. At 40 min, the value of working condition 4 increases by 46.19% compared with that of working condition 2, indicating that attention reaches the best at 50 dB noise environment, and the attenuability of attention is significantly enhanced at 90 dB noise environment. Stroop experiment showed that the time of working condition 2 is the shortest and the correct number is the most, and the reliability of working condition 4 is only 78.8%. The study on the effect of EEG on miners' attention under different noise levels can provide reference for the development of shift system and subsequent related research in mining enterprises.

Key words: electroencephalography (EEG), noise, miner's attention, rhythmic wave, feature extraction

LU Caiwu, GAO Ruiyang, XU Xiaohui, JIANG Song, LIU Di, FU Xinkai. Study on miners' attention under different noise conditions based on EEG data[J]. China Safety Science Journal, 2023, 33(5): 35-41.

Figures/Tables 10

Tab.1

Fig.1

Fig.2

Fig.3

Tab.2

Fig.4

Fig.5

Fig.6

Tab.3

Fig.7

References 26

[1]	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, 201, 21(6):2608-2616.
[2]	王浩, 蒋承林, 史莉莉. 煤矿井下噪声危害分析及对策[J]. 中国安全生产科学技术, 2011, 7(12):183-187.
	WANG Hao, JIANG Chenglin, SHI Lili. Analysis and countermeasures on underground noise hazards of coal mine[J]. Journal of Safety Science and Technology, 2011, 7(12):183-187.
[3]	栗婧, 王真, 秦亚茹, 等. 不同噪声强度对煤矿工人作业失误率的影响研究[J]. 中国安全科学学报, 2021, 31(2):179-184. doi: 10.16265/j.cnki.issn 1003-3033.2021.02.025
	LI Jing, WANG Zhen, QIN Yaru, et al. Study on influence of different noise intensity on operation error rate of mine workers[J]. China Safety Science Journal, 201, 31(2):179-184.
[4]	李乃文, 韩峥. 基于多主体建模的安全注意力衰减模型研究[J]. 中国安全科学学报, 2012, 22(12):51-57.
	LI Naiwen, HAN Zheng. Safety attention attenuation model based on multi-agents modeling[J]. China Safety Science Journal, 2012, 22(12):51-57.
[5]	郭孜政, 陈崇双, 陈亚青. 高负荷驾驶任务下驾驶员注意力状态概率模型[J]. 西南交通大学学报, 2009, 44(4):541-546.
	GUO Zizheng, CHEN Chongshuang, CHEN Yaqing. Probability model for driver attention state under high driving load[J]. Journal of Southwest Jiaotong University, 2009, 44(4):541-546.
[6]	王春雪, 吕淑然. 噪声对安全注意力影响实验研究[J]. 中国安全生产科学技术, 2016, 12(3):160-164.
	WANG Chunxue, LYU Shuran. Experimental study on influence to safety attention by noise[J]. Journal of Safety Science and Technology, 2016, 12(3):160-164.
[7]	MUNDORFF J S. Effects of speech signal type and attention on acceptable noise level in elderly, hearing-impaired listeners[D]. Harrisonburg, Virginia: James Madison University, 2011.
[8]	REBSAMEN B, BURDET E, GUAN C, et al. A brain-controlled wheelchair based on P300 and path guidance[C]. The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006:1101-1106.
[9]	白学军, 杨海波, 曾力, 等.一种穿戴式脑电注意力训练仪:中国, CN103656833B[P]. 2015-12-30.
[10]	CHO W, LEE M, LEE J, et al. An examination of the effects of various noises on physiological sensibility responses by using human EEG[J]. Journal of Mechanical Science and Technology, 2013, 27 (12): 3589-3593. doi: 10.1007/s12206-013-0908-y
[11]	李敏, 贾惠侨, 李开伟, 等. 噪声水平对人不安全行为的影响研究[J]. 中国安全科学学报, 2017, 27(3):19-24. doi: 10.16265/j.cnki.issn1003-3033.2017.03.004
	LI Min, JIA Huiqiao, LI Kaiway, et al. Effect of noise level on unsafe behavior of humans[J]. China Safety Science Journal, 2017, 27(3):19-24. doi: 10.16265/j.cnki.issn1003-3033.2017.03.004
[12]	GBZ/T 229.4—2012, 工作场所职业病危害作业分级第4部分:噪声[S].
	GBZ/T 229. 4-2012, Classification of occupational hazards at workplace Part 4:occupational exposure to noise[S].
[13]	程根银, 陈绍杰, 齐金龙, 等. 煤矿井下噪声对人的不安全行为影响分析研究[J]. 华北科技学院学报, 2014, 11(1):89-93.
	CHENG Genyin, CHEN Shaojie, QI Jinlong, et al. Influence of underground noise on people's unsafe behavior in coal mines[J]. Journal of North China Institute of Science and Technology, 2014, 11(1):89-93.
[14]	王云松, 林德杰. 基于小波包分析的图像去噪处理[J]. 计算技术与自动化, 2005(4):99-101.
	WANG Yunsong, LIN Dejie. Image denoise based on wavelet packet analyze[J]. Computing Technology and Automation, 2005(4):99-101.
[15]	LIU Yinxia, ZHOU Weidong, YUAN Qi, et al. Automatic seizure detection using wavelet transform and SVM in long term intracranial EEG[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 20(6):749-755. doi: 10.1109/TNSRE.2012.2206054 pmid: 22868635
[16]	刘冲, 赵海滨, 李春胜, 等. 脑电信号频带能量特征的提取方法及分类研究[J]. 系统仿真学报, 2012, 24(12):2496-2499.
	LIU Chong, ZHAO Haibin, LI Chunsheng, et al. Research on band power extraction and classification of EEG signal[J]. Journal of System Simulation, 2012, 24(12):2496-2499.
[17]	吴欢, 印想, 官金安. 频带能量与样本熵在注意力脑电信号中的对比研究[J]. 计算机与数字工程, 2020, 48(3):603-606,622.
	WU Huan, YIN Xiang, GUAN Jin'an. Comparative research of band energy and sample entropy in attention EEG signals[J]. Computer & Digital Engineering, 2020, 48(3):603-606,622.
[18]	ABASOLOL D, HORNEROL R, ESPINO P, et al. Entropy analysis of the EEG background activity in Alzheimer's disease patients[J]. Physiological Measurement, 2006, 27(3):241-253. doi: 10.1088/0967-3334/27/3/003 pmid: 16462011
[19]	刘慧, 和卫星, 陈晓平. 生物时间序列的近似熵和样本熵方法比较[J]. 仪器仪表学报, 2004, 25(增1):806-807,812.
	LIU Hui, HE Weixingi, CHEN Xiaoping. Approximate entropy and sample entropy for physiological time-series[J]. Chinese Journal of Scientific Instrument, 2004, 25(S1):806-807,812.
[20]	LALO E, GILBERTSON, DOYLE L, et al. Phasic increases in cortical beta activity are associated with alterations in sensory processing in the human[J]. Experimental Brain Research Volume, 2007, 177(1):137-145.
[21]	THOMPSON L, THOMPSON M, REID A. Neurofeedback outcomes in clients with Asperger's syndrome[J]. Applied Psychophysiology and Biofeedback, 2010, 35(1):63-81. doi: 10.1007/s10484-009-9120-3 pmid: 19908142
[22]	PETER P, BART V, ELSA A G, et al. EEG theta/beta ratio as a potential biomarker for attentional control and resilience against deleterious effects of stress on attention[J]. Cognitive Affective & Behavioral Neuroscience, 2014, 14(2): 782-791.
[23]	ISITEN N, CEBI M, SUTCUBASI B, et al. Medication effects on EEG biomarkers in attention-deficit/hyperactivity disorder[J]. Clin EEG Neurosci, 2017, 48(4): 246-250. doi: 10.1177/1550059416675232 pmid: 27798290
[24]	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.
[25]	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.
[26]	佟瑞鹏, 杨校毅. 行为安全损耗和激励双路径管理理论研究[J]. 中国安全科学学报, 2020, 30(9):8-14.
	TONG Ruipeng, YANG Xiaoyi. Dual path management theory of behavior based safety impairment and motivation[J]. China Safety Science Journal, 2020, 30(9):8-14.

设备或工作地点	噪声
采煤机	87
带式运输机	83
液压泵站	93
锚杆钻机	101
矿山工作办公室	39

脑波类型	频率/Hz	生理表现
δ波	0.5~4	“无意识”层面的波,一般只有深度睡眠状态下才会出现
θ波	4~8	“潜意识”层面的波,在人抑郁或试图抑制困意时出现
α波	8~13	“健康”波,放松、安静、闭眼的清醒状态下
β波	13~30	积极思考,专注且高度警觉时

Stroop试验	任务分组	工况1	工况2	工况3	工况4	对照组
应用时间/s	A	20.38±2.74	20.22±5.92	22.47±4.89	23.01±5.48	20.14±4.76
	B	18.59±3.60	18.30±4.16	20.31±5.40	20.99±4.75	18.29±2.91
	C	25.34±4.20	25.64±4.97	27.78±5.34	28.45±5.27	25.04±4.29
出错次数	A	4.70±2.76	4.66±3.53	5.04±3.53	5.14±3.16	4.60±2.81
	B	4.54±1.99	3.61±0.89	5.33±2.36	6.77±1.62	4.57±1.32
	C	6.26±4.89	5.02±3.81	6.65±4.77	7.18±3.58	5.16±4.83
平均可靠度/%	—	82.80	85.23	82.20	78.80	84.07