Abstract
The purpose of this study was to evaluate the psychoacoustic annoyance (PA) that the tractor drivers are exposed to, and investigate its effects on their brain signals during their work activities. To this aim, the sound of a garden tractor was recorded. Each driver’s electroencephalogram (EEG) was then recorded at five different engine speeds. The Higuchi method was used to calculate the fractal dimension of the brain signals. To evaluate the amount of acoustic annoyance that the tractor drivers were exposed to, a psychoacoustic annoyance (PA) model was used. The results showed that as the engine speed increased, the values of PA increased as well. The results also indicated that an increase in the Higuchi’s fractal dimension (HFD) of alpha and beta bands was due to the increase of the engine speed. The regression results also revealed that there was a high correlation between the HFD of fast wave activities and PA, in that, the coefficients of determination were 0.92 and 0.91 for alpha and beta bands, respectively. Hence, a good correlation between the EEG signals and PA can be used to develop a mathematical model which quantifies the human brain response to the external stimuli.Keywords:
EEG, Higuchi, fractal, tractor, sound.References
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3. Atli S.K. et al. (2019), Auditory event-related potentials demonstrate early cognitive impairment in children with subclinical hypothyroidism, Journal of Pediatric Endocrinology and Metabolism, 32(7): 689–697, https://doi.org/10.1515/jpem-2018-0463
4. Banerjee A. et al. (2016), Study on brain dynamics by non linear analysis of music induced EEG signals, Physica A: Statistical Mechanics and its Applications, 444: 110–120, https://doi.org/10.1016/j.physa.2015.10.030
5. Basner M. et al. (2014), Auditory and non-auditory effects of noise on health, The Lancet, 383(9925): 1325–1332, https://doi.org/10.1016/S0140-6736%2813%2961613-X
6. Bhoria R., Singal P., Verma D. (2012), Analysis of effect of sound levels on EEG, International Journal of Advanced Technology & Engineering Research (IJATER), 2(2): 121–124.
7. Bojic T., Vuckovic A., Kalauzi A. (2010), Modeling EEG fractal dimension changes in wake and drowsy states in humans – a preliminary study, Journal of Theoretical Biology, 262(2): 214–222, https://doi.org/10.1016/j.jtbi.2009.10.001
8. Boroujeni F. M., Maleki A. (2019), Fractal analysis of noise signals of Sampo and John Deere combine harvesters in operational conditions, Archives of Acoustics, 44(1): 89–98, https://doi.org/10.24425/aoa.2019.126355
9. Carletti E., Pedrielli F., Casazza C. (2011), Development and validation of a numerical prediction model to estimate the annoyance condition at the operation station of compact loaders, International Journal of Occupational Safety and Ergonomics, 17(3): 233–240, https://doi.org/10.1080/10803548.2011.11076889
10. Chen C., Li K., Wu Q., Wang H., Qian Z., Sudlow G. (2013), EEG-based detection and evaluation of fatigue caused by watching 3DTV, Displays, 34(2): 81–88, https://doi.org/10.1016/j.displa.2013.01.002
11. Chen X., Peng H., Yu F., Wang K. (2017), Independent vector analysis applied to remove muscle artifacts in EEG data, IEEE Transactions on Instrumentation and Measurement, 66(7): 1770–1779, https://doi.org/10.1109/TIM.2016.2608479
12. Dietrich A., Kanso R. (2010), A review of EEG, ERP, and neuroimaging studies of creativity and insight, Psychological Bulletin, 136(5): 822–848, https://doi.org/10.1037/a0019749
13. Eoh H.J., Chung M.K., Kim S.H. (2005), Electroencephalographic study of drowsiness in simulated driving with sleep deprivation, International Journal of Industrial Ergonomics, 35(4): 307–320, https://doi.org/10.1016/j.ergon.2004.09.006
14. Fastl H., Zwicker E. (2007), Psychoacoustics: Facts and models, 3rd ed., Springer-Verlag, Berlin.
15. Fujii K., Atagi J., Ando Y. (2002), Temporal and spatial factors of traffic noise and its annoyance, Journal of Temporal Design in Architecture and the Environment, 2(1): 33–41.
16. Ghaderi M., Javadikia H., Naderloo L., Mostafaei M., Rabbani H. (2019), Analysis of noise pollution emitted by stationary MF285 tractor using different mixtures of biodiesel, bioethanol, and diesel through artificial intelligence, Environmental Science and Pollution Research, 26(21): 21682–21692, https://doi.org/10.1007/s11356-019-05523-1
17. Gladun K.V. (2020), Higuchi fractal dimension as a method for assessing response to sound stimuli in patients with diffuse axonal brain injury, Modern Technologies in Medicine, 12(4): 63–70, https://doi.org/10.17691/stm2020.12.4.08
18. Han H.S. (2012), Psycho-acoustic evaluation of the indoor noise in cabins of a naval vessel using a backpropagation neural network algorithm, International Journal of Naval Architecture and Ocean Engineering, 4(4): 374–385, https://doi.org/10.2478/IJNAOE-2013-0104
19. Hettich D.T., Bolinger E., Matuz T., Birbaumer N., Rosenstiel W., Spüler M. (2016), EEG responses to auditory stimuli for automatic affect recognition, Frontiers in Neuroscience, 10(244): 1–10, https://doi.org/10.3389/fnins.2016.00244
20. Hinrikus H. et al. (2011), Higuchi’s fractal dimension for analysis of the effect of external periodic stressor on electrical oscillations in the brain, Medical & Biological Engineering & Computing, 49(5): 585–591, https://doi.org/10.1007/s11517-011-0768-5
21. Janssens K., Vecchio A., Van der Auweraer H. (2008), Synthesis and sound quality evaluation of exterior and interior aircraft noise, Aerospace Science and Technology, 12(1): 114–124, https://doi.org/10.1016/j.ast.2007.10.002
22. Jap B.T., Lal S., Fischer P., Bekiaris E. (2009), Using EEG spectral components to assess algorithms for detecting fatigue, Expert Systems with Applications, 36(2): 2352–2359, https://doi.org/10.1016/j.eswa.2007.12.043
23. Jiang X., Bian G.-B., Tian Z. (2019), Removal of artifacts from EEG signals: a review, Sensors, 19(5): 987, https://doi.org/10.3390/s19050987
24. Kesic S., Spasic S.Z. (2016), Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: a review, Computer Methods and Programs in Biomedicine, 133: 55–70, https://doi.org/10.1016/j.cmpb.2016.05.014
25. Khodabakhshi M., Saba V. (2018), The analysis of individuals emotions through brain signals using poincare approach, Paramedical Sciences and Military Health, 13(3): 12–19.
26. Klonowski W., Olejarczyk E., Stepien R. (2005), Sleep-EEG analysis using Higuchi’s fractal dimension, [in:] International Symposium on Nonlinear Theory and its Applications, pp. 18–21.
27. Kong W., Zhou Z., Jiang B., Babiloni F., Borghini G. (2017), Assessment of driving fatigue based on intra/inter-region phase synchronization, Neurocomputing, 219: 474–482, https://doi.org/10.1016/j.neucom.2016.09.057
28. Korn H., Faure P. (2003), Is there chaos in the brain? II. Experimental evidence and related models, Comptes Rendus Biologies, 326(9): 787–840, https://doi.org/10.1016/j.crvi.2003.09.011
29. Kropotov J.D. et al. (2000), Human auditory-cortex mechanisms of preattentive sound discrimination, Neuroscience Letters, 280(2): 87–90, https://doi.org/10.1016/S0304-3940%2800%2900765-5
30. Lalremruata, Dewangan K.N., Patel T. (2019), Noise exposure to tractor drivers in field operations, International Journal of Vehicle Performance, 5(4): 430–442, https://doi.org/10.1504/IJVP.2019.104085
31. Lashgari M., Maleki A. (2015), Psychoacoustic evaluation of a garden tractor noise, Agricultural Engineering International: CIGR Journal, 17(3): 231–241.
32. Lashgari M., Maleki A. (2016), Evaluation of lawn tractor noise using acoustic and psychoacoustic descriptors, Engineering in Agriculture, Environment and Food, 9(1): 116–122, https://doi.org/10.1016/j.eaef.2015.07.001
33. Lee Y.J., Shin T.J., Lee S.K. (2013), Sound quality analysis of a passenger car based on electroencephalography, Journal of Mechanical Science and Technology, 27(2): 319–325, https://doi.org/10.1007/s12206-012-1248-z
34. Lippé S., Martinez-Montes E., Arcand C., Lassonde M. (2009), Electrophysiological study of auditory development, Neuroscience, 164(3): 1108–1118, https://doi.org/10.1016/j.neuroscience.2009.07.066
35. Liu H., Zhang J., Guo P., Bi F., Yu H., Ni G. (2015), Sound quality prediction for engine-radiated noise, Mechanical Systems and Signal Processing, 56: 277–287, https://doi.org/10.1016/j.ymssp.2014.10.005
36. Luo J., Feng Z., Zhang J., Lu N. (2016), Dynamic frequency feature selection based approach for classification of motor imageries, Computers in Biology and Medicine, 75: 45–53, https://doi.org/10.1016/j.compbiomed.2016.03.004
37. Mai G., Minett J.W., Wang W.S.Y. (2016), Delta, theta, beta, and gamma brain oscillations index levels of auditory sentence processing, Neuroimage, 133: 516–528, https://doi.org/10.1016/j.neuroimage.2016.02.064
38. Mardi Z., Ashtiani S.N.M., Mikaili M. (2011), EEG-based drowsiness detection for safe driving using chaotic features and statistical tests, Journal of Medical Signals and Sensors, 1(2): 130–137.
39. Mazaheri A., Picton T.W. (2005), EEG spectral dynamics during discrimination of auditory and visual targets, Cognitive Brain Research, 24(1): 81–96, https://doi.org/10.1016/j.cogbrainres.2004.12.013
40. Mohammadi E., Kermani S., Golparvar M. (2018), Evaluation of chaos on electroencephalogram in different depths of anesthesia, Journal of Isfahan Medical School, 36(482): 601–606.
41. Mohd Radzi S.S., Asirvadam V.S., Yusoff M.Z. (2019), Fractal dimension and power spectrum of electroencephalography signals of sleep inertia state, [in:] IEEE Access, 7: 185879–185892, https://doi.org/10.1109/ACCESS.2019.2960852
42. Nagabushanam P., George S.T., Dolly D.R.J., Radha S. (2020), Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families, International Journal of Circuit Theory and Applications, 48(12): 2219–2241, https://doi.org/10.1002/cta.2856
Namazi H. (2018), Complexity based analysis of the correlation between external stimuli and bio signals, ARC Journal of Neuroscience, 3(3): 6–9.
44. Neuhaus A.H. et al. (2009), Acute dopamine depletion with branched chain amino acids decreases auditory top-down event-related potentials in healthy subjects, Schizophrenia Research, 111(1–3): 167–173, https://doi.org/10.1016/j.schres.2009.03.023
45. Nishifuji S., Sato M., Maino D., Tanaka S. (2010), Effect of acoustic stimuli and mental task on alpha, beta and gamma rhythms in brain wave, Proceedings of SICE Annual Conference, pp. 1548–1554.
46. Nor M.J.M., Fouladi M.H., Nahvi H., Ariffin A.K. (2008), Index for vehicle acoustical comfort inside a passenger car, Applied Acoustics, 69(4): 343–353, https://doi.org/10.1016/j.apacoust.2006.11.001
47. Park B., Jeon J. Y., Choi S., Park J. (2015), Short-term noise annoyance assessment in passenger compartments of high-speed trains under sudden variation, Applied Acoustics, 97: 46–53, https://doi.org/10.1016/j.apacoust.2015.04.007
48. Pavithra M., NiranjanaKrupa B., Sasidharan A., Kutty B.M., Lakkannavar M. (2014), Fractal dimension for drowsiness detection in brainwaves, International Conference on Contemporary Computing and Informatics (IC3I), pp. 757–761, https://doi.org/10.1109/IC3I.2014.7019676
49. Samiee S., Azadi S., Kazemi R., Nahvi A., Eichberger A. (2014), Data fusion to develop a driver drowsiness detection system with robustness to signal loss, Sensors, 14(9): 17832–17847, https://doi.org/10.3390/s140917832
50. Sanei S., Chambers J.A. (2013), EEG Signal Processing, John Wiley & Sons Ltd., England.
51. Siano D., Prati M.V., Costagliola M.A., Panza M.A. (2015), Evaluation of noise level inside cab of a bifuel passenger vehicle, WSEAS Transactions on Applied and Theoretical Mechanics, 10: 220–226.
52. Sink M., Hossain M., Kato T. (2011), Non-linear analysis of psychophysiological effects of auditory stimuli using fractal mining, Proceedings of the International Conference on Scientific Computing (CSC).
53. Sulaiman N., Taib M.N., Lias S., Murat Z.H., Aris S.A.M., Hamid N.H.A. (2011), Novel methods for stress features identification using EEG signals, International Journal of Simulation: Systems, Science and Technology, 12(1): 27–33, https://doi.org/10.5013/IJSSST.a.12.01.04
54. Sygna K., Aasvang G.M., Aamodt G., Oftedal B., Krog N.H. (2014), Road traffic noise, sleep and mental health, Environmental Research, 131: 17–24, https://doi.org/10.1016/j.envres.2014.02.010
55. van Kamp I., Davies H. (2013), Noise and health in vulnerable groups: a review, Noise and Health, 15(64): 153–159, https://doi.org/10.4103/1463-1741.112361
56. Vega C.F., Noel J. (2015), Parameters analyzed of Higuchi’s fractal dimension for EEG brain signals, [in:] 2015 Signal Processing Symposium (SPSympo), pp. 1–5, https://doi.org/10.1109/SPS.2015.7168285
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