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Derivation of EEG Spectrum-based Feature Parameters for Mental Fatigue Determination

정신적 피로 판별을 위한 뇌파 스펙트럼 기반 특징 파라미터 도출

  • Seo, Ssang-Hee (School of Computer Science and Engineering, Kyungnam University)
  • 서쌍희 (경남대학교 컴퓨터공학부)
  • Received : 2021.08.16
  • Accepted : 2021.10.20
  • Published : 2021.10.28

Abstract

In this paper, we tried to derive characteristic parameters that reflect mental fatigue through EEG measurement and analysis. For this purpose, mental fatigue was induced through a resting state with eyes closed and performing subtraction operations in mental arithmetic for 30 minutes. Five subjects participated in the experiment, and all subjects were right-handed male students in university, with an average age of 25.5 years. Spectral analysis was performed on the EEG collected at the beginning and the end of the experiment to derive feature parameters reflecting mental fatigue. As a result of the analysis, the absolute power of the alpha band in the occipital lobe and the temporal lobe increased as the mental fatigue increased, while the relative power decreased. Also, the difference in power between resting state and task state showed that the relative power was larger than the absolute power. These results indicate that alpha relative power in the occipital lobe and temporal lobe is a feature parameter reflecting mental fatigue. The results of this study can be utilized as feature parameters for the development of an automated system for mental fatigue determination such as fatigue and drowsiness while driving.

본 논문은 뇌파 측정 및 분석을 통해 정신적 피로를 반영하는 특징 파라미터를 도출하고자 하였다. 이를 위해 30분간 눈을 감은 편안한 안정 상태와 뺄셈연산을 암산으로 수행하는 작업을 통해 정신적 피로를 유도하였다. 5명의 피험자가 실험에 참가하였으며, 피험자들은 모두 대학 재학 중인 오른손잡이 남학생들이며 평균 나이는 25.5세이다. 정신적 피로를 반영하는 특징 파라미터 도출을 위해 실험 처음과 마지막에서 수집된 뇌파에 대해 스펙트럼분석을 수행하였다. 분석 결과, 정신적으로 피로할수록 후두엽 및 측두엽 위치에서 알파대역의 절대파워는 증가한 반면 상대파워는 감소하였다. 또한 안정 상태와 작업 상태간 파워 차이는 절대파워에 비해 상대파워가 크게 나타났다. 이 결과는 후두엽 및 측두엽 위치에서의 알파 상대파워가 정신적 피로를 반영하는 특징 파라미터임을 나타낸다. 본 연구 결과는 운전 중 피로 및 졸음 판단과 같은 정신적 피로 판별을 위한 자동화시스템 개발을 위한 특징 파라미터로 활용될 수 있다.

Keywords

Acknowledgement

This results was supported by "Regional Innovation Strategy(RIS)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE)(2021RIS-003)

References

  1. G. R. J. Hockey. (2003). Operator functional state: the assessment and prediction of human performance degradation in complex tasks. Amsterdam: IOS Press.
  2. R. Z. Guo et al. (2018). The impairing effects of mental fatigue on response inhibition: An ERP study. PLos ONE, 13(6), e0198206. DOI : 10.1371/journal.pone.0198206
  3. M. M. Lorist, M. A. Boksem & K. R. Ridderinkhof. (2005). Impaired cognitive control and reduced cingulate activity during mental fatigue. Brain Res Cogn Rain Res. 24(2), 199-205. DOI : 10.1016/j.cogbrainres.2005.01.018
  4. M. Boksem & M. Tops. (2008). Mental fatigue: costs and benefits. Brain Res Rev. 59(1), 125-139. DOI : 10.1016/j.brainresrev.2008.07.001
  5. S. G. Hart & I. E. Staveland. (1988). Development of nasa-tlx (task load index): Results of empirical and theoretical research. Advances in psychology, 52, 139-183. DOI : 10.1016/s0166-4115(08)62386-9
  6. T. Akerstedlt & M. Gillberg. (1990). Subjective and objective sleepiness in the active individual. International Journal of Neuroscience, 52(1-2), 29-37. DOI : 10.3109/00207459008994241
  7. M. W. Johns. (1991). A new method for measuring daytime sleepiness: the Epworth sleepiness scale. sleep, 14(6), 540-545. DOI : 10.1093/sleep/14.6.540
  8. T. Chalder et al. (1993). Development of a fatigue scale. Journal of psychosomatic research, 37(2), 147-153. DOI : 10.1016/0022-3999(93)90081-p
  9. W. Zhu, H. Yand, Y. Jin & B. Kiu. (2017). A method for recognizing fatigue driving based on dempster-shafer theory and fuzzy neural network. Mathematical Problems in Engineering, 2017 DOI : 10.1155/2017/6191035
  10. V. Menon, S. Rivera, C. White, G. glover & A. Reiss. (2000). Dissociating prefrontal and parietal cortex activation during arithmetic processing. Neuroimage, 12(4), 357-365. DOI : 10.1006/nimg.2000.0613
  11. S. W. Chuang, L. W. Ko, Y. P. Lin, R. S. Huang, T. P. Jung & C. T. Lin. (2012). Co-modulatory spectral changes in independent brain processes are correlated with task performance. Neuroimage, 62(3), 1469-1477. DOI : 10.1016/j.neuroimage.2012.05.035
  12. B. He, S. Gao, H. Yuan & J. R. Wolpaw. (2013). Brain-computer-interfaces. Neural Engineering. Boston : Springer.
  13. J. S, Kwon. (2000). The clinical utility of EEG mapping. Annals of Clinical Neurophysiology, 2(1), 41-46.
  14. C. Lafrance & M. Dumont. (2000). Diurnal variations in the waking EEG: comparison with sleep latencies and subjective alertness. Journal of Sleep Research, 9(3), 243-248. DOI : 10.1046/j.1365-2869.2000.00204.x
  15. M. A. Boksem, T. F. Meijman & M. M. Lorist. (2005). Effects of mental fatigue on attention: An ERP study. Cognitive Brain Research, 25(1), 107-116. DOI : 10.1016/j.cogbrainres.2005.04.011
  16. A. A. Putilov & O. G. Donskaya. (2014). Alpha attenuation soon after closing the eyes as an objective indicator of sleepiness. Clin Exp Pharmacol Physiol, 41(12), 956-964. DOI : 10.1111/1440-1681.12311
  17. A. Craig, Y. Tran, N. Wijesuriva & H. Nguyen. (2012). Regional brain wave activity changes associated with fatigue. Psychophysiology, 49(4), 574-582. DOI : 10.1111/j.1469-8986.2011.01329.x
  18. G. Li et al. (2020). The impact of mental fatigue on brain activity: a comparative study both in resting state and task state using EEG. BMC Neuroscience, 21(20). DOI : 10.1186/s12868-020-00569-1
  19. M. Adamou, T. Fullen & S. L. Jones. (2020). EEG for diagnosis of adult ADHD: a systematic review with narrative analysis. Front Psychiatry, 11, 871. DOI : 10.3389/fpsyt.2020.00871
  20. E. Magosso, F. D. Crescenzio, G. Ricci, S. Piastra & M. Ursino. (2019). EEG alpha power is modulated by attentional changes during cognitive tasks and virtual reality immersion. Computational Intelligence and Neuroscience, 2019. DOI : 10.1155/2019/7051079
  21. E. C. Thomeer, C. J. Stam & T. C. van Woerkom. (1994). EEG changes during mental activation. Clin Electroencephalogr, 25(3), 94-98. DOI : 10.1177/155005949402500305