Statistical Analysis of Noise-induced Brain Electrical Activity of Employees in the Underground Mining Sector in the Soma Basin

Year 2022, Volume: 18 Issue: 3, 331 - 339, 29.09.2022

Ayla Tekin , Mustafa Oğuz Nalbant , Mustafa Orhan , Fırat Tekin , Fatih Suvaydan , Kemal Berki , Sami Gümüş , Aslı Aydın Savran

https://doi.org/10.18466/cbayarfbe.1114348

Cited By: 1

Abstract

In the mining sector, which requires a lot of attention, excessive noise pollution is encountered during the works due to the use of mining machines, and this is observed as one of the most important factors causing various problems for the personnel working in underground mining.
The study investigated the neurological effects of instantaneous noise exposure and exposure to noise on workers' health in the underground mining sector using electroencephalography (EEG) device. Firstly, the noises that underground workers are exposed to in different working areas were determined. The brain’s electrical activities were measured at periodic intervals under the noise of one hundred people who work or will work in the mining industry. Their relationship with occupational noise exposure was analyzed statistically. As a result of these measurements, the values collected in noise-free and noisy environments were compared.

Keywords

Noise Effect, Underground Mining, Occupational Health, Statistical Analysis, EEG

Supporting Institution

Manisa Celal Bayar University Scientific Research Projects Coordination Unit

Project Number

2019-083

Thanks

This study was supported by Manisa Celal Bayar University Scientific Research Projects Coordination Unit within the scope of project no 2019-083.

References

• [1]. Akbay, D, Altındağ, R, Şengün, N. 2019. Geleneksel Yöntemle Açılan Karayolu Tünellerinde Çalışanların Gürültü Maruziyetlerinin Değerlendirilmesi. Politeknik Dergisi. DOI: 10.2339/politeknik.495339.
• [2]. Ikuharu M, Kazuhisa M, Shintaro T. 1997. Noise-Induced Hearing Loss in Working Environment and its Background, Journal of Occupational Health. 39(1), 5-17. DOI: https://doi.org/10.1539/joh.39.5.
• [3]. Nassiri, P, Monazam, M, DehaghiFouladi, B, Abadi, LIG, Zakerian, A. 2013. The effect of noise on human performance: a clinical trial, International Journal of Occupational and Environmental Health, 4, pp. 87-95.
• [4]. Monteiro, R, Tomé, D, Neves, P, Silva, D, Rodrigues, MA. 2018. Interactive effect of occupational noise on attention and short-term memory: a pilot study, Noise Health, 20, pp. 190-198.
• [5]. Tekin, A. 2020. Noise Exposure Estimation of Surface-Mine- Heavy Equipment Operators Using Artificial Neural Networks . Celal Bayar University Journal of Science , 16 (4) , 429-436. Retrieved from https://dergipark.org.tr/tr/pub/cbayarfbe/issue/58992/773051. [6]. Sensogut C. 2007. Occupational Noise in Mines and Its Control – A Case Study, Polish Journal of Environmental Studies. 16(6):939-942.
• [7]. Golmohammadi, R, Darvishi, E, Faradmal, J, Poorolajal, J, Aliabadi, M. 2020. Attention and short-term memory during occupational noise exposure considering task difficulty, Applied Acoustics, 158 107065, https://doi.org/10.1016/j. apacoust.2019.107065.
• [7]. Schmidt-Daffy, M. 2012. Velocity versus safety: impact of goal conflict and task difficulty on drivers’ behaviour, feelings of anxiety, and electrodermal responses, Transportation Research Part F: Traffic Psychology and Behaviour, 15 319–332, https://doi.org/10.1016/j. trf.2012.02.004.
• [9]. Ahn, CR, Lee, S, Sun, C, Jebelli, H, Yang, K, Choi, B. 2019. Wearable sensing technology applications in construction safety and health, Journal of Construction Engineering and Management, 145, 03119007, https://doi.org/10.1061/(asce)co.1943-7862.0001708.
• [10]. Yang, K, Ahn, C.R. 2019. Inferring workplace safety hazards from the spatial patterns of workers’ wearable data, Advanced Engineering Informatics, 41,100924, https://doi.org/ 10.1016/j.aei.2019.100924. [11]. Kim, H, Ahn, CR, Yang, K. 2017. Identifying safety hazards using collective bodily responses of workers, Journal of Construction Engineering and Management, 143, 04016090, https://doi. org/10.1061/(ASCE)CO.1943-7862.0001220.
• [12]. Kim, H, Ahn, CR, Yang, K. 2019. Validating ambulatory gait assessment technique for hazard sensing in construction environments, Automation in Construction, 98, 302–309, https://doi.org/10.1016/j.autcon.2018.09.017.
• [13]. Jeon, J, Cai, H, Yu, D, Xu, X. 2020. Identification of Safety Hazards Using Wearable EEG, Construction Research Congress, 2020, American Society of Civil Engineers, Reston, VA, pp. 185–194, https://doi.org/10.1061/9780784482872.021.
• [14]. Choi, B, Jebelli, H, Lee, S. 2019. Feasibility analysis of electrodermal activity (EDA) acquired from wearable sensors to assess construction workers’ perceived risk, Safety Science, 115, 110–120, https://doi.org/10.1016/j.ssci.2019.01.022.
• [15]. Olbrich, S, Mulert, C, Karch, S, Trenner, M, Leicht, G, Pogarell, O, Hegerl, U. 2009. EEGvigilance and BOLD effect during simultaneous EEG/fMRI measurement, NeuroImage 45, 319–332, https://doi.org/10.1016/j.neuroimage.2008.11. 014.
• [16]. Zhai, J, Chen, X, Ma, J, Yang, Q, Liu, Y. 2016. The vigilance-avoidance model of avoidant recognition: an ERP study under threat priming, Psychiatry Research, 246, 379–386, https://doi.org/10.1016/j.psychres.2016.10.014.
• [17]. Eoh, HJ, Chung, MK, Kim, SH. 2005. Electroencephalographic study of drowsiness in simulated driving with sleep deprivation, International Journal of Industrial Ergonomics, 35, 307–320, https://doi.org/10.1016/j.ergon.2004.09.006.
• [18]. Aryal, A, Ghahramani, A, Becerik-Gerber, B. 2017. Monitoring fatigue in construction workers using physiological measurements, Automation in Construction, 82, 154–165, https://doi.org/10.1016/j.autcon.2017.03.003.
• [19]. Ikenishi, T, Kamada, T, Nagai, M. 2013. Analysis of longitudinal driving behaviors during car following situation by the driver's EEG using PARAFAC, IFAC Proc, Vol. 46, 415–422, https://doi.org/10.3182/20130811-5-US-2037.00023.
• [20]. Kawashima, I, Kumano, H. 2017. Prediction of mind-wandering with electroencephalogram and non-linear regression modeling, Frontiers in Human Neuroscience, 11, pp. 1-10, https://doi.org/10.3389/fnhum.2017.00365.
• [21]. Jeon, J, Cai, H. 2021. Classification of construction hazard-related perceptions using: Wearable electroencephalogram and virtual reality. Automation in Construction, 132, 103975.
• [22]. Bo, Y, Chao, W, Ji, L, Huimin, L. 2014. Physiological responses of people in working faces of deep underground mines. International Journal of Mining Science and Technology. 24(5), 683-688, https://doi.org/10.1016/j.ijmst.2014.03.024.
• [23]. Bashashati, A, Fatourechi, M, Ward, RK, Birch, GE. 2007. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals, Journal of Neural Engineering, 4, R32–R57, https://doi.org/10.1088/1741-2560/4/2/R03.
• [24]. Zauner, A, Fellinger, R, Gross, J, Hanslmayr, S, Shapiro, K, Gruber, W, Müller, S, Klimesch, W. 2021. Alpha entrainment is responsible for the attentional blink phenomenon, NeuroImage 63 674–686, https://doi.org/10.1016/j. neuroimage.2012.06.075.
• [25]. Ke, J, et al. 2021. Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device. Automation in Construction, 125, 103598.