China Safety Science Journal ›› 2023, Vol. 33 ›› Issue (5): 35-41.doi: 10.16265/j.cnki.issn1003-3033.2023.05.2228

• Safety social science and safety management • Previous Articles     Next Articles

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

LU Caiwu1,2(), GAO Ruiyang1,2,**(), XU Xiaohui1,2, JIANG Song1,2, LIU Di1,2, FU Xinkai3   

  1. 1 School of Resource Engineering, Xi 'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
    2 Xi'an Key Laboratory for Intelligent Industrial Perception, Calculation and Decision, Xi'an Shaanxi 710055, China
    3 Sinosteel Group, Fuquan Mining Co., Ltd., Ji'ning Shandong 272500, China
  • Received:2022-12-15 Revised:2023-03-14 Online:2023-05-28 Published:2023-11-28
  • Contact: GAO Ruiyang

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