Mental Fatigue Assessment using recording Brain Signals: Electroencephalography

Fouladi Dehaghi, Behzad; Mohammadi, Abbas; Nematpour, Leila

doi:10.30699/jergon.7.2.45

Write your message

ارسال به ایمیل

Volume 7, Issue 2 (Iranian Journal of Ergonomics 2019) Iran J Ergon 2019, 7(2): 45-53 | Back to browse issues page

‎ 10.30699/jergon.7.2.45

Mendeley

Zotero

RefWorks

Fouladi Dehaghi B, Mohammadi A, Nematpour L. Mental Fatigue Assessment using recording Brain Signals: Electroencephalography. Iran J Ergon 2019; 7 (2) :45-53
URL: http://journal.iehfs.ir/article-1-625-en.html

Mental Fatigue Assessment using recording Brain Signals: Electroencephalography

Behzad Fouladi Dehaghi¹

, Abbas Mohammadi²

, Leila Nematpour ^*

1- Associate Professor, Department of Occupational Safety and Health Engineering, Faculty of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
2- Assistant Professor, Department of Occupational Safety and Health Engineering, Faculty of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
3- MSc of Occupational Health, Department of Occupational Safety and Health Engineering, Faculty of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran , Lnematpour94@gmail.com

Abstract: (7905 Views)

Background and Objectives: Mental fatigue is a condition triggered by prolonged cognitive activity. Mental fatigue causes brain over-activity. This is a condition where the brain cells become exhausted, hampering person productivity, and overall cognitive function. The aim of this study was to assess students’ mental fatigue using brain indices.
Methods: The present descriptive - analytic study has been conducted on 20 students of the Faculty of Health mean age (SD) of 24.40 (3.73) years old in Ahwaz University of Medical Sciences (2019). To assess the performance of the participants, they were asked to study a text with spelling errors and correct those errors. This activity was performed in five stages, each lasting 15 min and EEG was recorded at all stages, and at each stage, the visual analog scale was completed by participants. Data analysis was done by SPSS 24.
Results: The results showed that the activity of alpha, beta, and theta signals in the first 15 minutes was 0.89±0.30, 0.70±0.33, and 1.19±0.36, and the last 15 minutes, 0.63±0.34, 0.55±0.26, and 1.03±0.34 respectively. Reducing the activity of the signals indicated there has been an increase in the amount of mental fatigue in individuals. Also, using visual analog scale, the individuals have acknowledged that they have experienced symptoms of mental fatigue. Finally, there was no significant relationship between students’ EEG and visual analog scale.
Conclusion: The results showed that alpha, beta and theta indices could be suitable indicators for evaluating mental fatigue. Also, mental fatigue can be one of the factors that affect the accuracy and performance of individuals, so that it can reduce their attention and efficiency.

Keywords: Electroencephalography (EEG), Visual Analog Scale, Mental fatigue, Accuracy and performance

Full-Text [PDF 383 kb] (8881 Downloads) | | Extended Abstract (HTML) (5740 Views)

Type of Study: Research | Subject: Other Cases
Received: 2019/03/30 | Accepted: 2019/06/30 | ePublished: 2019/11/11

References

1. Wright RA, Stewart CC, Barnett BR. Mental fatigue influence on effort-related cardiovascular response: Extension across the regulatory (inhibitory)/non-regulatory performance dimension. International Journal of Psychophysiology. 2008 Aug 1;69(2):127-33. [DOI:10.1016/j.ijpsycho.2008.04.002] [PMID]

2. Halvani GH, Baghianimoghadam MH, Rezaei MH. Fatigue situation in tile industries workers. Iran Occupational Health. 2007 Sep 15;4(3):57-63. [Article] [Google Scholar]

3. van der Linden D. The urge to stop: The cognitive and biological nature of acute mental fatigue. Cognitive fatigue: Multidisciplinary perspectives on current research and future applications. 2011:149-64. [DOI:10.1037/12343-007]

4. Faber LG, Maurits NM, Lorist MM. Mental fatigue affects visual selective attention. PloS one. 2012 Oct 31;7(10):e48073. [DOI:10.1371/journal.pone.0048073] [PMID] [PMCID]

5. Van der Linden D, Eling P. Mental fatigue disturbs local processing more than global processing. Psychological research. 2006 Sep 1;70(5):395-402. [DOI:10.1007/s00426-005-0228-7] [PMID]

6. Firoozeh M, Kavousi A, Hasanzadeh S. Evaluation of Relationship between Occupational Exposure to Organic Solvent and Fatigue Workers at a Paint Factory in Saveh city. Iran Occupational Health. 2017 Jun 15;14(2):82-92. [Article] [Google Scholar]

7. Ghasemkhani M, Abbasinia M, Mahmoodkhani S, Aghaee H, Asghari M. Assessment of fatigue and its relationship with Insomnia Severity Index in shift workers, fixed and rotating, Tehran rolling mills and steel production company. Iran Occupational Health. 2013 Jul 15;10(2):79-86. [Google Scholar]

8. Vecchiato G, Astolfi L, Fallani FD, Cincotti F, Mattia D, Salinari S, Soranzo R, Babiloni F. Changes in brain activity during the observation of TV commercials by using EEG, GSR and HR measurements. Brain topography. 2010 Jun 1;23(2):165-79. [DOI:10.1007/s10548-009-0127-0] [PMID]

9. Chuckravanen D, Rajbhandari S, Bester A. Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. 2015 Jul 20;5(1-4):46-59. [Google Scholar]

10. Shen KQ, Ong CJ, Li XP, Hui Z, Wilder-Smith EP. A feature selection method for multilevel mental fatigue EEG classification. IEEE transactions on biomedical engineering. 2007 Jun 18;54(7):1231-7. [DOI:10.1109/TBME.2007.890733] [PMID]

11. Shankar H, Pesudovs K. Critical flicker fusion test of potential vision. Journal of Cataract & Refractive Surgery. 2007 Feb 1;33(2):232-9. [DOI:10.1016/j.jcrs.2006.10.042] [PMID]

12. Arghami S, Ghoreishi A, Kamali K, Farhadi M. Investigating the consistency of mental fatigue measurements by visual analog scale (vas) and flicker fusion apparatus. Iranian Journal of Ergonomics. 2013 Sep 15;1(1):66-72. [Google Scholar]

13. Xu R, Zhang C, He F, Zhao X, Qi H, Zhou P, Zhang L, Ming D. How physical activities affect mental fatigue based on EEG energy, connectivity, and complexity. Frontiers in neurology. 2018;9. [DOI:10.3389/fneur.2018.00915] [PMID] [PMCID]

14. Rajavenkatanarayanan A, Kanal V, Tsiakas K, Brady J, Calderon D, Wylie G, Makedon F. Towards a robot-based multimodal framework to assess the impact of fatigue on user behavior and performance: a pilot study. InProceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments 2019 Jun 5 ;493-8. ACM. [DOI:10.1145/3316782.3322776]

15. Nematpour L. Investigating mental fatigue and its effect on the performance of the faculty of Health staff by Electroencephalographic Signals. Journal of Occupational Hygiene Engineering.:45-53. [Article]

16. Zhao C, Zhao M, Liu J, Zheng C. Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator. Accident Analysis & Prevention. 2012 Mar 1;45:83-90. [DOI:10.1016/j.aap.2011.11.019] [PMID]

17. Boksem MA, Meijman TF, Lorist MM. Effects of mental fatigue on attention: an ERP study. Cognitive brain research. 2005 Sep 1;25(1):107-16. [DOI:10.1016/j.cogbrainres.2005.04.011] [PMID]

18. Bijur PE, Silver W, Gallagher EJ. Reliability of the visual analog scale for measurement of acute pain. Academic emergency medicine. 2001 Dec;8(12):1153-7. [DOI:10.1111/j.1553-2712.2001.tb01132.x] [PMID]

19. Nedelcu E, Portase R, Tolas R, Muresan R, Dinsoreanu M, Potolea R, editors. Artifact detection in EEG using machine learning. 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP); 2017: IEEE. [DOI:10.1109/ICCP.2017.8116986]

20. Hsu BW, Wang MJ, Chen CY, Chen F. Effective indices for monitoring mental workload while performing multiple tasks. Perceptual and motor skills. 2015 Aug;121(1):94-117. [DOI:10.2466/22.PMS.121c12x5] [PMID]

21. Zhang C, Zheng C, Yu X. Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms. Chinese Science Bulletin. 2008 Jun 1;53(12):1835-47. [DOI:10.1007/s11434-008-0245-1]

22. Fan X, Zhou Q, Liu Z, Xie F. Electroencephalogram assessment of mental fatigue in visual search. Bio-medical materials and engineering. 2015 Jan 1;26(s1):S1455-63. [DOI:10.3233/BME-151444] [PMID]

23. Craig A, Tran Y, Wijesuriya N, Nguyen H. Regional brain wave activity changes associated with fatigue. Psychophysiology. 2012 Apr;49(4):574-82. [DOI:10.1111/j.1469-8986.2011.01329.x] [PMID]

24. Pires FO, Silva-Júnior FL, Brietzke C, Franco-Alvarenga PE, Pinheiro FA, de França NM, Teixeira S, Meireles Santos T. Mental fatigue alters cortical activation and psychological responses, impairing performance in a distance-based cycling trial. Frontiers in physiology. 2018 Mar 16;9:227. [DOI:10.3389/fphys.2018.00227] [PMID] [PMCID]

25. Trejo LJ, Kubitz K, Rosipal R, Kochavi RL, Montgomery LD. EEG-based estimation and classification of mental fatigue. Psychology. 2015 Apr 3;6(05):572. [DOI:10.4236/psych.2015.65055]

26. Charbonnier S, Roy RN, Bonnet S, Campagne A. EEG index for control operators' mental fatigue monitoring using interactions between brain regions. Expert Systems with Applications. 2016 Jun 15;52:91-8. [DOI:10.1016/j.eswa.2016.01.013]

27. Smith MR, Chai R, Nguyen HT, Marcora SM, Coutts AJ. Comparing the effects of three cognitive tasks on indicators of mental fatigue. The Journal of psychology. 2019 Jun 6:1-25. [DOI] [Google Scholar]

28. Talukdar U, Hazarika SM, Gan JQ. Motor imagery and mental fatigue: inter-relationship and EEG based estimation. Journal of computational neuroscience. 2018: 1-22. [DOI:10.1007/s10827-018-0701-0] [PMID]

29. Eoh HJ, Chung MK, Kim SH. Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. International Journal of Industrial Ergonomics. 2005 Apr 1;35(4):307-20. [DOI:10.1016/j.ergon.2004.09.006]

30. Jap BT, Lal S, Fischer P. Comparing combinations of EEG activity in train drivers during monotonous driving. Expert Systems with Applications. 2011 Jan 1;38(1):996-1003. [DOI:10.1016/j.eswa.2010.07.109]

31. Hsu BW, Wang MJ. Evaluating the effectiveness of using electroencephalogram power indices to measure visual fatigue. Perceptual and motor skills. 2013 Feb;116(1):235-52. [DOI:10.2466/29.15.24.PMS.116.1.235-252] [PMID]

32. Rosipal R, Trejo LJ, Zaidel E. Atomic Decomposition of EEG for Mapping Cortical Activation. InTensor Methods for Machine Learning Workshop 2013 Sep. [Article] [Google Scholar]

33. Niedermeyer E, da Silva FL. Electroencephalography: basic principles, clinical applications, and related fields: Lippincott Williams & Wilkins; 2005.

34. Gergelyfi M, Jacob B, Olivier E, Zénon A. Dissociation between mental fatigue and motivational state during prolonged mental activity. Frontiers in behavioral neuroscience. 2015 Jul 13;9:176. [DOI:10.3389/fnbeh.2015.00176] [PMID] [PMCID]

35. Bowyer SM, Hsieh L, Moran JE, Young RA, Manoharan A, Liao CC, Malladi K, Yu YJ, Chiang YR, Tepley N. Conversation effects on neural mechanisms underlying reaction time to visual events while viewing a driving scene using MEG. Brain research. 2009 Jan 28;1251:151-61. [DOI:10.1016/j.brainres.2008.10.001] [PMID] [PMCID]

36. Hsieh L, Young RA, Bowyer SM, Moran JE, Genik II RJ, Green CC, Chiang YR, Yu YJ, Liao CC, Seaman S. Conversation effects on neural mechanisms underlying reaction time to visual events while viewing a driving scene: fMRI analysis and asynchrony model. Brain Research. 2009 Jan 28;1251:162-75. [DOI:10.1016/j.brainres.2008.10.002] [PMID]