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Real-Time Mental Workload Estimation Using EEG

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Human Mental Workload: Models and Applications (H-WORKLOAD 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1107))

Abstract

Tracking mental workload in real-time during worker’s performance is a challenge, as it requires the worker to report during the task execution. Moreover, it is thus based on subjective experience. Neuroergonomics tackles this issue, by employing neurophysiological metrics to obtain objective, real-time information. We measured mental workload (MWL) derived from electroencephalography (EEG) signals, for subjects engaged in a simulated computer-based airplane-landing task. To test this metric, we calculated the degree of correlation between measured MWL and observable variables associated to task complexity. In the two settings of the experiment, we used a 24-channel full-cap EEG system and the novel mobile EEG headphone device. The latter allows seamless integration of the EEG acquisition system in a possible real-world setup scenario. Obtained results reveal significant correlation between the EEG derived MWL metric and the two objective task complexity metrics: the number of airplanes on the screen subjects had to control, as well as the number of actions performed by the subject during the task in both setups. Therefore, this work represents a proof of concept for using the proposed systems for reliable real-time mental workload tracking.

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References

  1. Recarte, M.A., Nunes, L.M.: Mental workload while driving: effects on visual search, discrimination, and decision making. J. Exp. Psychol. Appl. 9(2), 119 (2003)

    Article  Google Scholar 

  2. Gawron, V.J.: Human Performance Measures Handbook. Lawrence Erlbaum Associates Publishers, New Jersey (2000)

    Google Scholar 

  3. Paas, F.G., Van Merriënboer, J.J.: Variability of worked examples and transfer of geometrical problem-solving skills: a cognitive-load approach. J. Educ. Psychol. 86(1), 122 (1994)

    Article  Google Scholar 

  4. Yeh, Y.Y., Wickens, C.D.: Dissociation of performance and subjective measures of workload. Hum. Factors 30(1), 111–120 (1988)

    Article  Google Scholar 

  5. Reid, G.B., Nygren, T.E.: The subjective workload assessment technique: a scaling procedure for measuring mental workload. Adv. Psychol. 52, 185–218 (1988)

    Article  Google Scholar 

  6. Peterson, D.A., Kozhokar, D.: Peak-end effects for subjective mental workload ratings. Proc. Hum. Factors Ergon. Soc. Ann. Meet. 61(1), 2052–2056 (2017)

    Article  Google Scholar 

  7. Kapoor, A., Burleson, W., Picard, R.W.: Automatic prediction of frustration. Int. J. Hum. Comput. Stud. 65(8), 724–736 (2007)

    Article  Google Scholar 

  8. Gevins, A., Smith, M.E.: Neurophysiological measures of cognitive workload during human-computer interaction. Theor. Issues Ergon. Sci. 4(1–2), 113–131 (2003)

    Article  Google Scholar 

  9. Kramer, A.F.: Physiological metrics of mental workload: a review of recent progress. In: Damos, D.L. (ed.) Multiple-Task Performance, pp. 279–328. Taylor and Francis, London (1991)

    Google Scholar 

  10. Brookings, J.B., Wilson, G.F., Swain, C.R.: Psychophysiological responses to changes in workload during simulated air traffic control. Biol. Psychol. 42(3), 361–377 (1996)

    Article  Google Scholar 

  11. Gevins, A., Smith, M.E., McEvoy, L., Yu, D.: High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb. Cortex 7(4), 374–385 (1997)

    Article  Google Scholar 

  12. Missonnier, P., et al.: Frontal theta event-related synchronization: comparison of directed attention and working memory load effects. J. Neural Transm. 113(10), 1477–1486 (2006)

    Article  Google Scholar 

  13. Stipacek, A., Grabner, R.H., Neuper, C., Fink, A., Neubauer, A.C.: Sensitivity of human EEG alpha band desynchronization to different working memory components and increasing levels of memory load. Neurosci. Lett. 353(3), 193–196 (2003)

    Article  Google Scholar 

  14. Holm, A., Lukander, K., Korpela, J., Sallinen, M., Müller, K.M.: Estimating brain load from the EEG. Sci. World J. 9, 639–651 (2009)

    Article  Google Scholar 

  15. Berka, C., et al.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(5), B231–B244 (2007)

    Google Scholar 

  16. Hogervorst, M.A., Brouwer, A.M., Van Erp, J.B.: Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload. Front. Neurosci. 8, 322 (2014)

    Article  Google Scholar 

  17. Debener, S., Minow, F., Emkes, R., Gandras, K., De Vos, M.: How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology 49(11), 1617–1621 (2012)

    Article  Google Scholar 

  18. De Vos, M., Kroesen, M., Emkes, R., Debener, S.: P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier. J. Neural Eng. 11(3), 036008 (2014)

    Article  Google Scholar 

  19. Mijović, P., Ković, V., De Vos, M., Mačužić, I., Todorović, P., Jeremić, B.: Towards continuous and real-time attention monitoring at work: reaction times versus brain response. Ergonomics 60(2), 241–254 (2017)

    Article  Google Scholar 

  20. Parasuraman, R., Rizzo, M.: Neuroergonomics: The Brain at Work. Oxford University Press, New York (2008)

    Google Scholar 

  21. Mijović, P., Milovanović, M., Ković, V., Gligorijević, I., Mijović, B., Mačužić, I.: Neuroergonomics method for measuring the influence of mental workload modulation on cognitive state of manual assembly worker. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 213–224. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61061-0_14

    Chapter  Google Scholar 

  22. Kramer, A.F., Parasuraman, R.: Neuroergonomics: applications of neuroscience to human factors. In: Cacciopo, J., Tassinary, L.G., Berntson, G.G. (eds.) Handbook of Psychophysiology. Cambridge University Press, New York (2005)

    Google Scholar 

  23. Mijović, P., Ković, V., De Vos, M., Mačužić, I., Jeremić, B., Gligorijević, I.: Benefits of instructed responding in manual assembly tasks: an ERP approach. Front. Hum. Neurosci. 10, 171 (2016)

    Article  Google Scholar 

  24. NATS games: ATC landing. https://www.nats.aero/careers/trainee-air-traffic-controllers/games/game/atclanding/. Accessed Aug 2019

  25. Lab Streaming Layer. https://github.com/sccn/labstreaminglayer. Accessed Aug 2019

  26. XDF: TheExtensible Data Format Based on XML Concepts. https://nssdc.gsfc.nasa.gov/nssdc_news/june01/xdf.html. Accessed Aug 2019

  27. https://www.mbraintrain.com/wp-content/uploads/2016/01/RBE-24-STD.pdf. Accessed Aug 2019

  28. mBrainTrain: Smartfones. https://mbraintrain.com/smartfones/. Accessed Aug 2019

  29. Swartz Center for Computational Neuroscience: EEGLAB. https://sccn.ucsd.edu/eeglab/index.php. Accessed Aug 2019

  30. Mullen, T.R., et al.: Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Biomed. Eng. 62(11), 2553–2567 (2015)

    Article  Google Scholar 

  31. Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999)

    Article  Google Scholar 

  32. Park, J., Seager, T.P., Rao, P.S.C., Convertino, M., Linkov, I.: Integrating risk and resilience approaches to catastrophe management in engineering systems. Risk Anal. 33(3), 356–367 (2013)

    Article  Google Scholar 

  33. Dekker, S.: The Field Guide to Understanding Human Error. Ashgate, Hampshire (2016)

    Google Scholar 

  34. Comberti, L., Leva, M.C., Demichela, M., Desideri, S., Baldissone, G., Modaffari, F.: An empirical approach to workload and human capability assessment in a manufacturing plant. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2018. CCIS, vol. 1012, pp. 180–201. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14273-5_11

    Chapter  Google Scholar 

  35. Leva, M.C., Caimo, A., Duane, R., Demichela, M., Comberti, L.: Task complexity, and operators’ capabilities as predictor of human error: modeling framework and an example of application. In: Haugen, S., Barros, A., van Gulijk, C., Kongsvik, T., Vinnem, J.E. (eds.) Safety and Reliability–Safe Societies in a Changing World: Proceedings of ESREL. CRC Press, Boca Raton (2018)

    Google Scholar 

  36. Longo, L.: Subjective usability, mental workload assessments and their impact on objective human performance. In: Bernhaupt, R., Dalvi, G., Joshi, A., Balkrishan, D.K., O’Neill, J., Winckler, M. (eds.) INTERACT 2017. LNCS, vol. 10514, pp. 202–223. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67684-5_13

    Chapter  Google Scholar 

  37. Rizzo, L., Longo, L.: Representing and inferring mental workload via defeasible reasoning. In: Proceedings of the 1st Workshop on Advances in Argumentation in Artificial Intelligence (2017)

    Google Scholar 

  38. Longo, L., Leva, M.C. (eds.): H-WORKLOAD 2018. CCIS, vol. 1012. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14273-5

    Book  Google Scholar 

  39. Longo, L., Leva, M.C. (eds.): H-WORKLOAD 2017. CCIS, vol. 726. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61061-0

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. mBrainTrain LLC, Požarevačka 36, 11000, Belgrade, Serbia

    Aneta Kartali, Ivan Gligorijević, Pavle Mijović & Bogdan Mijović

  2. School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000, Belgrade, Serbia

    Aneta Kartali & Milica M. Janković

  3. School of Environmental Health, Technological University Dublin, Kevin Street, Dublin 2, Ireland

    Maria Chiara Leva

Authors
  1. Aneta Kartali
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  2. Milica M. Janković
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  3. Ivan Gligorijević
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  4. Pavle Mijović
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  5. Bogdan Mijović
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  6. Maria Chiara Leva
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Corresponding authors

Correspondence to Milica M. Janković , Ivan Gligorijević , Pavle Mijović , Bogdan Mijović or Maria Chiara Leva .

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Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Technological University Dublin, Dublin, Ireland

    Maria Chiara Leva

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Kartali, A., Janković, M.M., Gligorijević, I., Mijović, P., Mijović, B., Leva, M.C. (2019). Real-Time Mental Workload Estimation Using EEG. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2019. Communications in Computer and Information Science, vol 1107. Springer, Cham. https://doi.org/10.1007/978-3-030-32423-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-32423-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32422-3

  • Online ISBN: 978-3-030-32423-0

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