A Review on Applications of Soft Computing Techniques in Neuroergonomics During the Last Decade

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)


Soft computing (SC) methods play an important role in addressing the different types of problems and offering potential alternatives at the present time. Such methods have also been implemented in the context of neuroergonomics, because of the success of SC strategies, to reliably evaluate the mental workload and achieve better results than traditional approaches. Nevertheless, these applications are still limited. This paper surveys SC techniques using classification and literature review of articles for the last decade (2009–2019) to explore how various SC methodologies have been developed during this period. The purpose of this paper is to summarize the results through a systemic review of current research papers on the use of SC methodologies in neuroergonomics. Throughout the course of this study, it has been observed that SC techniques have been applied to most traditional areas of neuroergonomics research, and research in neuroergonomics has grown in recent years.


Neuroergonomics Soft computing Review 


  1. 1.
    Parasuraman, R., Rizzo, M. (eds.): Neuroergonomics: The Brain at Work, vol. 3. Oxford University Press, New York (2008)Google Scholar
  2. 2.
    Nauck, D., Klawonn, F., Kruse, R.: Foundations of Neuro-Fuzzy Systems. Wiley, New York (1997)zbMATHGoogle Scholar
  3. 3.
    Parasuraman, R., Hancock, P.A.: Adaptive control of mental workload. In: Hancock, P.A., Desmond, P.A. (eds.) Stress, Workload, and Fatigue, pp. 305–333. Lawrence Erlbau, Mahwah (2001)Google Scholar
  4. 4.
    Ayaz, H., Dehais, F.: Neuroergonomics: The Brain at Work and Everyday Life, 1st edn. Elsevier, Academic Press, Cambridge (2019)Google Scholar
  5. 5.
    Kum, S., Furusho, M., Duru, O., Satir, T.: Mental workload of the VTS operators by utilising heart rate. Trans. Nav. 1(2), 145–151 (2007)Google Scholar
  6. 6.
    Nachreiner, F.: Standards for ergonomics principles relating to the design of work systems and to mental workload. Appl. Ergon. 26(4), 259–263 (1995)CrossRefGoogle Scholar
  7. 7.
    Moray, N.: Mental workload since 1979. Int. Rev. Ergon. 2, 123–150 (1988)Google Scholar
  8. 8.
    Liang, G.F., Lin, J.T., Hwang, S.L., Huang, F.H., Yenn, T.C., Hsu, C.C.: Evaluation and prediction of on-line maintenance workload in nuclear power plant. Hum. Fact. Ergon. Manuf. 19(1), 64–77 (2009)CrossRefGoogle Scholar
  9. 9.
    Wu, Y., Liu, Z., Jia, M., Tran, C.C., Yan, S.: Using artificial neural networks for predicting mental workload in nuclear power plants based on eye tracking. Nucl. Technol. 206(1), 94–106 (2020)CrossRefGoogle Scholar
  10. 10.
    Yan, S., Wei, Y., Tran, C.C.: Evaluation and prediction mental workload in user interface of maritime operations using eye response. Int. J. Ind. Ergon. 71, 117–127 (2019)CrossRefGoogle Scholar
  11. 11.
    Yan, S., Tran, C.C., Wei, Y., Habiyaremye, J.L.: Driver’s mental workload prediction model based on physiological indices. Int. J. occup. Saf. Ergon. 25(3), 476–484 (2019)CrossRefGoogle Scholar
  12. 12.
    Chen, Y., Yan, S., Tran, C.C.: Comprehensive evaluation method for user interface design in nuclear power plant based on mental workload. Nucl. Eng. Technol. 51(2), 453–462 (2019)CrossRefGoogle Scholar
  13. 13.
    Yong, D.: Subjective mental workload assessment based on generalized fuzzy numbers. Cybern. Syst. Int. J. 42(4), 246–263 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Saadati, M., Nelson, J., Ayaz, H.: Mental workload classification from spatial representation of FNIRS recordings using convolutional neural networks. In: 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE (2019)Google Scholar
  15. 15.
    Saadati, M., Nelson, J., Ayaz, H.: Convolutional neural network for hybrid fNIRS-EEG mental workload classification. In: International Conference on Applied Human Factors and Ergonomics, pp. 221–232. Springer, Cham (2019)Google Scholar
  16. 16.
    Liu, Y., Ayaz, H., Shewokis, P.A.: Multisubject “learning” for mental workload classification using concurrent EEG, fNIRS, and physiological measures. Frontiers Hum. Neurosci. 11, 389 (2017)CrossRefGoogle Scholar
  17. 17.
    Elkin, C., Devabhaktuni, V.: Comparative analysis of machine learning techniques in assessing cognitive workload. In: International Conference on Applied Human Factors and Ergonomics, pp. 185–195. Springer, Cham (2019)Google Scholar
  18. 18.
    Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448 (2015)
  19. 19.
    Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M., Eggensperger, K., Tangermann, M., Ball, T.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38(11), 5391–5420 (2017)CrossRefGoogle Scholar
  20. 20.
    Trakoolwilaiwan, T., Behboodi, B., Lee, J., Kim, K., Choi, J.W.: Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain–computer interface: three-class classification of rest, right-, and left-hand motor execution. Neurophotonics 5(1), 011008 (2017)CrossRefGoogle Scholar
  21. 21.
    Hong, K.S., Naseer, N., Kim, Y.H.: Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI. Neurosci. Lett. 587, 87–92 (2015)CrossRefGoogle Scholar
  22. 22.
    Samima, S., Sarma, M.: EEG-based mental workload estimation. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5605–5608. IEEE (2019)Google Scholar
  23. 23.
    Zhang, P., Wang, X., Chen, J., You, W., Zhang, W.: Spectral and temporal feature learning with two-stream neural networks for mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27(6), 1149–1159 (2019)CrossRefGoogle Scholar
  24. 24.
    Zhang, Y., Shen, Y.: Parallel mechanism of spectral feature-enhanced maps in EEG-based cognitive workload classification. Sensors 19(4), 808 (2019)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Zhang, P., Wang, X., Zhang, W., Chen, J.: Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27(1), 31–42 (2018)CrossRefGoogle Scholar
  26. 26.
    Islam, M.R., Barua, S., Ahmed, M.U., Begum, S., Di Flumeri, G.: Deep learning for automatic EEG feature extraction: an application in drivers’ mental workload classification. In: International Symposium on Human Mental Workload: Models and Applications, pp. 121–135. Springer, Cham (2019)Google Scholar
  27. 27.
    Aghajani, H., Garbey, M., Omurtag, A.: Measuring mental workload with EEG + fNIRS. Frontiers Hum. Neurosci. 11, 359 (2017)CrossRefGoogle Scholar
  28. 28.
    Lee, M.H., Fazli, S., Mehnert, J., Lee, S.W.: Hybrid brain-computer interface based on EEG and NIRS modalities. In: 2014 International Winter Workshop on Brain-Computer Interface (BCI), pp. 1–2. IEEE (2014)Google Scholar
  29. 29.
    Gu, H., Yin, Z., Zhang, J.: EEG based mental workload assessment via a hybrid classifier of extreme learning machine and support vector machine. In: 2019 Chinese Control Conference (CCC), pp. 8398–8403. IEEE (2019)Google Scholar
  30. 30.
    Ting, P.H., Hwang, J.R., Doong, J.L., Jeng, M.C.: Driver fatigue and highway driving: a simulator study. Physiol. Behav. 94(3), 448–453 (2018)CrossRefGoogle Scholar
  31. 31.
    Liu, Y.T., Lin, Y.Y., Wu, S.L., Hsieh, T.Y., Lin, C.T.: Assessment of mental fatigue: an EEG-based forecasting system for driving safety. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3233–3238. IEEE (2015)Google Scholar
  32. 32.
    Ed-doughmi, Y., Idrissi, N.: Driver fatigue detection using recurrent neural networks. In: Proceedings of the 2nd International Conference on Networking, Information Systems & Security, pp. 1–6 (2019)Google Scholar
  33. 33.
    Chai, R., Tran, Y., Craig, A., Ling, S.H., Nguyen, H.T.: Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1318–1341. IEEE (2014)Google Scholar
  34. 34.
    Warm, J.S.: The psychophysics of vigilance. In: Proceedings of the Human Factors Society Annual Meeting, vol. 24, no. 1, p. 605. SAGE Publications, Los Angeles (1980)Google Scholar
  35. 35.
    Wu, W., Wu, Q.J., Sun, W., Yang, Y., Yuan, X., Zheng, W.L., Lu, B.L.: A regression method with subnetwork neurons for vigilance estimation using EOG and EEG. IEEE Trans. Cogn. Dev. Syst. (2018)Google Scholar
  36. 36.
    Rigane, O., Abbes, K., Abdelmoula, C., Masmoudi, M.: A fuzzy based method for driver drowsiness detection. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 143–147. IEEE (2017)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Department of Industrial EngineeringGazi UniversityAnkaraTurkey
  2. 2.Department of Industrial Engineering and Management SystemsUniversity of Central FloridaOrlandoUSA

Personalised recommendations