Effect of Psychoacoustic Annoyance on EEG Signals of Tractor Drivers

• Autorzy: Lashgari Majid , Arab Mohammad Reza , Nadjafi Mohsen , Rafiee Mojtaba

Identyfikatory

DOI

10.24425/aoa.2022.142899

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

EEG; Higuchi; fractal; tractor; sound.

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2022
• Tom: Vol. 47, No. 4
• Strony: 469--477
• Opis fizyczny: Bibliogr. 60 poz., fot., rys., tab., wykr.

Twórcy

autor

Lashgari Majid

• Department of Biosystems Engineering, Arak University Arak, Iran

autor

Arab Mohammad Reza

• Department of Medical Engineering, Arak University of Medical Sciences Arak, Iran

autor

Nadjafi Mohsen

• Department of Electrical Engineering, Arak University of Technology Arak, Iran

autor

Rafiee Mojtaba

• Department of Biosystems Engineering, Arak University Arak, Iran

Bibliografia

• 1. Allen P. (2000), Acoustics and Psychoacoustics. Audiology Diagnosis, Thieme Medical Publisher Inc., New York.
• 2. Al-Nuaimi A.H., Jammeh E., Sun L., Ifeachor E. (2017), Higuchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer’s disease, [in:] 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2320-2324, doi: 10.1109/EMBC.2017.8037320.
• 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, doi: 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, doi: 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, doi: 10.1016/S0140-6736(13)61613-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. Bojić 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 10.3390/s19050987.
• 24. Kesić 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, doi: 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, doi: 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, doi: 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, doi: 10.1016/S0304-3940(00)00765-5.
• 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 10.1002/cta.2856.
• 43. 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 10.1109/SPS.2015.7168285.
• 57. Xu R. et al. (2018), How physical activities affect mental fatigue based on EEG energy, connectivity, and complexity, Frontiers in Neurology, 9(915): 1-13, doi: 10.3389/fneur.2018.00915.
• 58. Yeo M.V.M., Li X., Shen K., Wilder-Smith E.P.V. (2009), Can SVM be used for automatic EEG detection of drowsiness during car driving?, Safety Science, 47(1): 115-124, doi: 10.1016/j.ssci.2008.01.007.
• 59. Zappasodi F., Marzetti L., Olejarczyk E., Tecchio F., Pizzella V. (2015), Age-related changes in electroencephalographic signal complexity, PLOS ONE, 10(11): e0141995, doi: 10.1371/journal.pone.0141995.
• 60. Zhang C., Wang W., Chen C., Zeng C., Anderson D.E., Cheng B. (2018), Determination of optimal electroencephalography recording locations for detecting drowsy driving, IET Intelligent Transport Systems, 12(5): 345-350, doi: 10.1049/iet-its.2017.0083.