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Fiziksel Performans Etkisinde Mental Aktivitenin Elektrofizyolojik Bulgularının Değerlendirilmesi

Year 2019, Volume: 7 Issue: 3, 389 - 397, 28.09.2019
https://doi.org/10.21541/apjes.520952

Abstract

Bu çalışmada, zihinsel işyükü paradigmasının yorgunluk ile ilintisine yönelik elektrofizyolojik ölçüm sonuçları iki farklı grup üzerinden incelenmiştir. Ayrık grup olarak, sporcular ve sedanterler ele alınmış olup, bu grupların beyin dinlenim durumu bulguları ile mental performans esnasındaki gerçek zamanlı elektrofizyolojik ölçümleri karşılaştırılmış ve bu bulgulardaki değişimler incelenmiştir. Mental aritmetik işlem ile zihinsel işyükü yaratılarak, kontrollü bir fiziksel performans öncesinde ve sonrasında VO2max değeri ve eşzamanlı EEG, PPG, EDA ve EKG ölçümleri gerçekleştirilmiştir. Farklı gruplara ait dinlenim durumu mental performansları ve fiziksel aktivite sonrasında oluşan mental işyükü arasındaki kalp atım hızı değişkenliği, oksijen tüketimi miktarı tartışılmıştır. Bulgularda, zihinsel işyükü süresince ölçümlenen EEG’de alfa frekansında baskılanma ile elektriksel deri direncinde artış görülmüştür. Ölçümlenen kalp atım hızı verileri parametrik olmayan istatistik test ile incelenmiş ve 0.15–0.4 Hz arası aralığındaki dinlenim durumu için istatistiksel anlamlı bulgular elde edilmiştir (p<0.05). Sonuç olarak, zihinsel aritmetik işyükünün kontrollü yorgunluk sağlanmış ölçümlerde, elektriksel beyin aktivitesinde alfa tutulumuna yol açtığı gözlemlenmiş ve ayrıca alfa değişiminin sporcu grup içinde daha az olduğu saptanmıştır. PPG sinyalleri değerlendirildiğinde, sporculardaki değişimin daha az olduğu, ayrıca sporcu gönüllülere ait PPG sinyallerindeki değişimin varyasyon sergileyebileceği gözlemlenmiştir. Raporlanan öncül bulgular doğrultusunda, fiziksel yorgunluk şartlarında incelenen iki grup için, sporcuların odaklanmalarının daha başarılı olduğu ve zihinsel işyükünden daha az etkilendikleri öngörülebilmektedir.

References

  • 1. P.A. Hancock, J.S. Warm, “A Dynamic Model of Stress and Sustained Attention.” Human Factors and Ergonomics Society 31, 519-537, 1989.
  • 2. B. Dunst, M. Benedek, E. Jauk, S. Bergner, K. Koschutnig, M. Sommer, A. Ischebeck, B. Spinath, M. Arendasy, M., Bühner, H. Freudenthaler, A.C. Neubauer, “Neural efficiency as a function of task demands.” Intelligence, 42, 22–30, 2014.
  • 3. A.C. Neubauer, A. Fink, “Intelligence and neural efficiency.” Neurosci. Biobehav. Rev., 33(7), 1004–23, 2009.
  • 4. T.H. Balcıoğlu, D. Şahin, M. Assem, S.B. Selman, D. Göksel Duru, “Göz Hareketleri Takibi ile Elit Sporcularda Bakış Karakteristikleri Analizi: Pilot Çalışma.” 18. Ulusal Biyomedikal Mühendisliği Toplantısı Kitapçığı, s. 1-4, 2014.
  • 5. M. Chaouachi, I. Jraidi, C. Frasson, “Modeling Mental Workload using EEG Features for Intelligent Systems.” Intl. Conf. User Modeling, Adaptation, Personalization, p. 50-61, 2011.
  • 6. D.C. Lefebvre, Y. Marchanda, G.A. Eskes, J.F. Connolly, “Assessment of working memory abilities using an event-related brain potential (ERP)-compatible digit span backward task,” Clinical Neurophysiology 116, 1665–1680, 2005.
  • 7. A. Gevins, M.E. Smith, “Neurophysiological measures of cognitive workload during human-computer interaction.” Theor. Issues Ergon. Sci. 4, 113–131, 2003.
  • 8. A.S. Smit, P.A. Eling, M.T. Hopman, A.M. Coenen, “Mental and physical effort affect vigilance differently,” Int J Psychophysiol. 57(3):211-7. Epub 2005 Apr 8, 2005.
  • 9. W. Klimesch, M. Doppelmayr, T. Pachinger, B. Ripper, “Brain oscillations and human memory: EEG correlates in the upper alpha and theta band. Neurosci. Lett. 238, 9–12, 1997.
  • 10. J.B. Brookings, G.F. Wilson, C.R. Swain, “Psychophysiological responses to changes in workload during simulated air traffic control.” Biol. Psychol. 42, 361–377, 1996.
  • 11. A. Gevins, M.E. Smith, L. McEvoy, D. Yu, “High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb. Cortex N. Y. N, 7, 374–385, 1997.
  • 12. L. Venables, S.H. Fairclough, “The influence of performance feedback on goal-setting and mental effort regulation. Motiv. Emot. 33, 63–74. doi:10.1007/s11031-008-9116-y, 2009.
  • 13. N. Jaušovec, K. Jaušovec, “Working memory training: improving intelligence--changing brain activity.” Brain Cogn. 79, 96–106, 2012.
  • 14. J. Onton, A. Delorme, S., Makeig, “Frontal midline EEG dynamics during working memory.” NeuroImage 27, 341–356, 2005.
  • 15. W. Klimesch, M. Doppelmayr, T. Pachinger, B. Ripper, “Brain oscillations and human memory: EEG correlates in the upper alpha and theta band.” Neurosci. Lett. 238, 9–12, 1997.
  • 16. W. Klimesch, M. Doppelmayr, H. Schimke, B. Ripper. “Theta Synchronization and Alpha Desynchronization in a Memory Task.” Psychophysiology, 34(2-1997), 169-76, 1997.
  • 17. W. Klimesch, M. Doppelmayr, H. Russegger, T. Pachinger, J. Schwaiger. “Induced alpha band power changes in the human EEG and attention.” Neurosci. Lett. 244: 73–76, 1998.
  • 18. W. Klimesch, “EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis.” Brain Res. Brain Res. Rev. 29, 169–195, 1999.
  • 19. D.A. Valentino, J.E. Arruda, S.M. Gold, “Comparison of QEEG and Response Accuracy in Good vs. Poorer Performers During a Vigilance Task.” International Journal of Psychophysiology, 15, p. 123–134, 1993.
  • 20. S.H. Fairclough, L. Venables and A. Tattersall, “The influence of task demand and learning on the psychophysiological response.” International Journal of Psychophysiology, vol. 56, n. 2, p. 171–184, 2005.
  • 21. G.F. Wilson, “Air-to-ground training missions: a psychophysiological workload analysis,” Ergonomics, vol. 36, n. 9, p. 1071–1087, 1993.
  • 22. A.H. Roscoe, “Assessing pilot workload. Why measure heart rate, HRV and respiration?, “Biol Psychol, vol. 34, n. 2–3, p. 259–287, 1992.
  • 23. A.H. Roscoe, “Heart rate as a psychophysiological measure for in-flight workload assessment,” Ergonomics, vol. 36, n. 9, p. 1055–1062, 1993.
  • 24. G.F. Wilson, P. Fullenkamp and I. Davis, “Evoked potential, cardiac, blink, and respiration measures of pilot workload in air-to-ground missions,” Aviat Space Environ Med, vol. 65, n. 2, p. 100–105, 1994.
  • 25. J.A. Veltman and A.W. Gaillard, “Physiological indices of workload in a simulated flight task,” Biol Psychol, vol. 42, n. 3, p. 323–342, 1996.
  • 26. T.C. Hankins and G.F. Wilson, “A comparison of heart rate, eye activity, EEG and subjective measures of pilot mental workload during flight,” Aviat Space Environ Med, vol. 69(4), p. 360–367, 1998.
  • 27. G.F. Wilson, C.R. Swain and P. Ullsperger, “EEG power changes during a multiple level memory retention task,” Int J Psychophysiol, vol. 32, n. 2, p. 107–118, 1999.
  • 28. D.L. Eckberg, “Human sinus arrhythmia as an index of vagal cardiac outflow,” J. Appl. Physiol., cilt 54, s. 961-66, 1985.
  • 29. S. Zhang, S. Hu, H.H. Chao, X. Luo, O.M. Farr, and C.R. Li, “Cerebral correlates of skin conductance responses in a cognitive task” Neuroimage. 62(3): 1489–1498, 2012.
  • 30. A. Hızal, C. Açıkada, T. Hazır, C. Tınazcı, “Modifiye Mekik Koşusu Testinin Güvenilirliği ve Geçerliği,” Spor Bilimleri Dergisi Hacettepe Journal of Sport Sciences, cilt 8, sayı 4, s. 3-12, 1997.
  • 31. A.D. Flouris, G.S. Metsios, Y. Koutedakis. “Enhancing the efficacy of the 20 m multistage shuttle run test,” British. J. Sports Medicine, cilt 39, s. 166–170, 2005.
  • 32. H. Jasper H. “Report of the Committee on Methods of Clinical Examination in Electroencephalography. Electroenceph. Clin. Neurophysiol.,10:370–375, 1958.
  • 33. S. Dong, L.M. Reder, Y. Yao, Y. Liu, F. Chen, “Individual differences in working memory capacity are reflected in different ERP and EEG patterns to task difficulty”, Brain Res. 2015 Aug 7;1616:146-56, Epub 2015 May 11.
  • 34. J.J. Carr and J.M. Brown, “Introduction to Biomedical Equipment Technology”, Prentice-Hall, Upper Saddle River, NJ, USA, 1998.
  • 35. T.L. Rusch, et al., “Signal processing methods for pulse oximetry,” Comput. Biol. Med., vol. 26, no. 2, pp. 143-159, 1996.
  • 36. S.M.L. Silva, R. Giannetti, R., et al., “Heuristic Algorithm for Photoplethysmographic Heart Rate Tracking During Maximal Exercise Test”, Journal of Medical and Biological Engineering, 32(3): 181-188, 2011.
  • 37. J.G. Proakis and D.K. Manolakis, “Digital Signal Processing, 4th Edition by (Publisher: Pearson; 4 edition, April 7, 2006.

Assessment of Electrophysiological Findings during Mental Workload in the Effect of Physical Performance

Year 2019, Volume: 7 Issue: 3, 389 - 397, 28.09.2019
https://doi.org/10.21541/apjes.520952

Abstract

The present study examines the findings of electrophysiological measurements of two groups related to the correlation of the mental workload paradigm to tiredness. Athletes and non-athletes (sedentary) are investigated as discrete groups. The aim here is to investigate the findings of the resting state brain network and the electrophysiologic measurements during mental performance of these groups and the changes and variations related to these findings. Mental workload is achieved via mental arithmetic backward counting, while aerobic capacity VO2max, EEG, PPG, EDA and ECG are being measured simultaneously, before and after physical performance. The study also discusses the oxygen consumption rate and the heart rate variability (HRV) between the resting state mental performance and the mental workload occurred after physical activity of varying groups. Thus, findings on the investigation of the effects of physical tiredness on the mental workload of the subjects from two different groups, can be summarized as follows: Increase in the alpha suppression and electrodermal activity during mental work performance has been detected. The heart rate variability data has been investigated with nonparametric statistical test. Statistical test results of resting state for eye closed paradigm for frequency range of 0.15–0.4 Hz are determined as statistically significant (p<0.05). Alpha frequency suppression is detected as a result for brain electrical activity analysis which are in agreement with literature. The EEG alpha activity detected while mental performance reflects more suppression than the eyes closed resting state alpha activity. To conclude, EEG alpha suppression is caused by mental arithmetic workload in volunteers with controlled physical performance, and the alpha suppression within sportsmen group is less, where the suppression of alpha activity obtained in sportsmen EEG is more explicit than in sedentary. Assessing the PPG signals results less variation in sportsmen. Results specify that PPG signals of sportsmen group represent less variances than PPG data of sedentary group. Also another finding specifies, that PPG signals within group may vary like in sportsmen group. According to these preliminary results, it can be predicted that the athlete group is less affected by mental workload despite being more focused and physically tired.

References

  • 1. P.A. Hancock, J.S. Warm, “A Dynamic Model of Stress and Sustained Attention.” Human Factors and Ergonomics Society 31, 519-537, 1989.
  • 2. B. Dunst, M. Benedek, E. Jauk, S. Bergner, K. Koschutnig, M. Sommer, A. Ischebeck, B. Spinath, M. Arendasy, M., Bühner, H. Freudenthaler, A.C. Neubauer, “Neural efficiency as a function of task demands.” Intelligence, 42, 22–30, 2014.
  • 3. A.C. Neubauer, A. Fink, “Intelligence and neural efficiency.” Neurosci. Biobehav. Rev., 33(7), 1004–23, 2009.
  • 4. T.H. Balcıoğlu, D. Şahin, M. Assem, S.B. Selman, D. Göksel Duru, “Göz Hareketleri Takibi ile Elit Sporcularda Bakış Karakteristikleri Analizi: Pilot Çalışma.” 18. Ulusal Biyomedikal Mühendisliği Toplantısı Kitapçığı, s. 1-4, 2014.
  • 5. M. Chaouachi, I. Jraidi, C. Frasson, “Modeling Mental Workload using EEG Features for Intelligent Systems.” Intl. Conf. User Modeling, Adaptation, Personalization, p. 50-61, 2011.
  • 6. D.C. Lefebvre, Y. Marchanda, G.A. Eskes, J.F. Connolly, “Assessment of working memory abilities using an event-related brain potential (ERP)-compatible digit span backward task,” Clinical Neurophysiology 116, 1665–1680, 2005.
  • 7. A. Gevins, M.E. Smith, “Neurophysiological measures of cognitive workload during human-computer interaction.” Theor. Issues Ergon. Sci. 4, 113–131, 2003.
  • 8. A.S. Smit, P.A. Eling, M.T. Hopman, A.M. Coenen, “Mental and physical effort affect vigilance differently,” Int J Psychophysiol. 57(3):211-7. Epub 2005 Apr 8, 2005.
  • 9. W. Klimesch, M. Doppelmayr, T. Pachinger, B. Ripper, “Brain oscillations and human memory: EEG correlates in the upper alpha and theta band. Neurosci. Lett. 238, 9–12, 1997.
  • 10. J.B. Brookings, G.F. Wilson, C.R. Swain, “Psychophysiological responses to changes in workload during simulated air traffic control.” Biol. Psychol. 42, 361–377, 1996.
  • 11. A. Gevins, M.E. Smith, L. McEvoy, D. Yu, “High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb. Cortex N. Y. N, 7, 374–385, 1997.
  • 12. L. Venables, S.H. Fairclough, “The influence of performance feedback on goal-setting and mental effort regulation. Motiv. Emot. 33, 63–74. doi:10.1007/s11031-008-9116-y, 2009.
  • 13. N. Jaušovec, K. Jaušovec, “Working memory training: improving intelligence--changing brain activity.” Brain Cogn. 79, 96–106, 2012.
  • 14. J. Onton, A. Delorme, S., Makeig, “Frontal midline EEG dynamics during working memory.” NeuroImage 27, 341–356, 2005.
  • 15. W. Klimesch, M. Doppelmayr, T. Pachinger, B. Ripper, “Brain oscillations and human memory: EEG correlates in the upper alpha and theta band.” Neurosci. Lett. 238, 9–12, 1997.
  • 16. W. Klimesch, M. Doppelmayr, H. Schimke, B. Ripper. “Theta Synchronization and Alpha Desynchronization in a Memory Task.” Psychophysiology, 34(2-1997), 169-76, 1997.
  • 17. W. Klimesch, M. Doppelmayr, H. Russegger, T. Pachinger, J. Schwaiger. “Induced alpha band power changes in the human EEG and attention.” Neurosci. Lett. 244: 73–76, 1998.
  • 18. W. Klimesch, “EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis.” Brain Res. Brain Res. Rev. 29, 169–195, 1999.
  • 19. D.A. Valentino, J.E. Arruda, S.M. Gold, “Comparison of QEEG and Response Accuracy in Good vs. Poorer Performers During a Vigilance Task.” International Journal of Psychophysiology, 15, p. 123–134, 1993.
  • 20. S.H. Fairclough, L. Venables and A. Tattersall, “The influence of task demand and learning on the psychophysiological response.” International Journal of Psychophysiology, vol. 56, n. 2, p. 171–184, 2005.
  • 21. G.F. Wilson, “Air-to-ground training missions: a psychophysiological workload analysis,” Ergonomics, vol. 36, n. 9, p. 1071–1087, 1993.
  • 22. A.H. Roscoe, “Assessing pilot workload. Why measure heart rate, HRV and respiration?, “Biol Psychol, vol. 34, n. 2–3, p. 259–287, 1992.
  • 23. A.H. Roscoe, “Heart rate as a psychophysiological measure for in-flight workload assessment,” Ergonomics, vol. 36, n. 9, p. 1055–1062, 1993.
  • 24. G.F. Wilson, P. Fullenkamp and I. Davis, “Evoked potential, cardiac, blink, and respiration measures of pilot workload in air-to-ground missions,” Aviat Space Environ Med, vol. 65, n. 2, p. 100–105, 1994.
  • 25. J.A. Veltman and A.W. Gaillard, “Physiological indices of workload in a simulated flight task,” Biol Psychol, vol. 42, n. 3, p. 323–342, 1996.
  • 26. T.C. Hankins and G.F. Wilson, “A comparison of heart rate, eye activity, EEG and subjective measures of pilot mental workload during flight,” Aviat Space Environ Med, vol. 69(4), p. 360–367, 1998.
  • 27. G.F. Wilson, C.R. Swain and P. Ullsperger, “EEG power changes during a multiple level memory retention task,” Int J Psychophysiol, vol. 32, n. 2, p. 107–118, 1999.
  • 28. D.L. Eckberg, “Human sinus arrhythmia as an index of vagal cardiac outflow,” J. Appl. Physiol., cilt 54, s. 961-66, 1985.
  • 29. S. Zhang, S. Hu, H.H. Chao, X. Luo, O.M. Farr, and C.R. Li, “Cerebral correlates of skin conductance responses in a cognitive task” Neuroimage. 62(3): 1489–1498, 2012.
  • 30. A. Hızal, C. Açıkada, T. Hazır, C. Tınazcı, “Modifiye Mekik Koşusu Testinin Güvenilirliği ve Geçerliği,” Spor Bilimleri Dergisi Hacettepe Journal of Sport Sciences, cilt 8, sayı 4, s. 3-12, 1997.
  • 31. A.D. Flouris, G.S. Metsios, Y. Koutedakis. “Enhancing the efficacy of the 20 m multistage shuttle run test,” British. J. Sports Medicine, cilt 39, s. 166–170, 2005.
  • 32. H. Jasper H. “Report of the Committee on Methods of Clinical Examination in Electroencephalography. Electroenceph. Clin. Neurophysiol.,10:370–375, 1958.
  • 33. S. Dong, L.M. Reder, Y. Yao, Y. Liu, F. Chen, “Individual differences in working memory capacity are reflected in different ERP and EEG patterns to task difficulty”, Brain Res. 2015 Aug 7;1616:146-56, Epub 2015 May 11.
  • 34. J.J. Carr and J.M. Brown, “Introduction to Biomedical Equipment Technology”, Prentice-Hall, Upper Saddle River, NJ, USA, 1998.
  • 35. T.L. Rusch, et al., “Signal processing methods for pulse oximetry,” Comput. Biol. Med., vol. 26, no. 2, pp. 143-159, 1996.
  • 36. S.M.L. Silva, R. Giannetti, R., et al., “Heuristic Algorithm for Photoplethysmographic Heart Rate Tracking During Maximal Exercise Test”, Journal of Medical and Biological Engineering, 32(3): 181-188, 2011.
  • 37. J.G. Proakis and D.K. Manolakis, “Digital Signal Processing, 4th Edition by (Publisher: Pearson; 4 edition, April 7, 2006.
There are 37 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Dilek Göksel Duru 0000-0003-1484-8603

Elif Işıkçı Koca This is me 0000-0002-2636-1467

Publication Date September 28, 2019
Submission Date February 1, 2019
Published in Issue Year 2019 Volume: 7 Issue: 3

Cite

IEEE D. Göksel Duru and E. Işıkçı Koca, “Fiziksel Performans Etkisinde Mental Aktivitenin Elektrofizyolojik Bulgularının Değerlendirilmesi”, APJES, vol. 7, no. 3, pp. 389–397, 2019, doi: 10.21541/apjes.520952.