Abstract
Many studies have shown that most maritime accidents are attributed to human error as the initiating cause, resulting in a need for study of human factors to improve safety in maritime transportation. Among the various techniques, Electroencephalography (EEG) has the key advantage of high time resolution, with the possibility to continuously monitor brain states including human mental workload, emotions, stress levels, etc. In this paper, we proposed a novel mental workload recognition algorithm using deep learning techniques that outperformed the state-of art algorithms and successfully applied it to monitor crew members’ brain states in a maritime simulator. We designed and carried out an experiment to collect the EEG data, which was used to study stress and distribution of mental workload among crew members during collaborative tasks in the ship’s bridge simulator. The experiment consisted of two parts. In part 1, 3 maritime trainees fulfilled the tasks with and without an experienced captain. The results of EEG analyses showed that 2 out of 3 trainees had less workload and stress when the experienced captain was present. In part 2, 4 maritime trainees collaborated with each other in the simulator. Our findings showed that the trainee who acted as the captain had the highest stress and workload levels while the other three trainees experienced low workload and stress due to the shared work and responsibility. These results suggest that EEG is a promising evaluation tool applicable in human factors study for the maritime domain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Liu, Y., et al.: EEG-based mental workload and stress recognition of crew members in maritime virtual simulator: a case study. In: International Conference on Cyberworlds Chester (2017)
Liu, Y., et al.: Human factors evaluation in maritime virtual simulators using mobile EEG-based neuroimaging. In: Transdisciplinary Engineering: A Paradigm Shift: Proceedings of the 24th ISPE Inc. International Conference on Transdisciplinary Engineering, 10–14 July 2017. IOS Press (2017)
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)
Folkman, S.: Stress: appraisal and coping. In: Gellman, M.D., Turner, J.R. (eds.) Encyclopedia of Behavioral Medicine. Springer, New York (2013). https://doi.org/10.1007/978-1-4419-1005-9
Hetherington, C., Flin, R., Mearns, K.: Safety in shipping: the human element. J. Saf. Res. 37(4), 401–411 (2006)
Arenius, M., Athanassiou, G., Sträter, O.: Systemic assessment of the effect of mental stress and strain on performance in a maritime ship-handling simulator. IFAC Proc. Vol. 43(13), 43–46 (2010)
Lützhöft, M.H., Dekker, S.W.: On your watch: automation on the bridge. J. Navig. 55(01), 83–96 (2002)
Baker, C.C., Seah, A.K.: Maritime accidents and human performance: the statistical trail. In: MarTech Conference, Singapore (2004)
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (eds.) Advances in Psychology, pp. 139–183. North-Holland, Amsterdam (1988)
Reid, G.B., Nygren, T.E.: The subjective workload assessment technique: a scaling procedure for measuring mental workload. Adv. Psychol. 52, 185–218 (1988)
Brouwer, A.-M., et al.: Estimating workload using EEG spectral power and ERPs in the n-back task. J. Neural Eng. 9(4), 045008 (2012)
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)
Yin, Z., Zhang, J.: Cross-session classification of mental workload levels using EEG and an adaptive deep learning model. Biomed. Signal Process. Control 33, 30–47 (2017)
Lim, W.L., et al.: EEG-based mental workload recognition related to multitasking. In: Proceeding of the International Conference on Information, Communications and Signal Processing (ICICS) (2015)
Hou, X., et al.: CogniMeter: EEG-based emotion, mental workload and stress visual monitoring. In: International Conference on Cyberworlds (2015)
Liu, Y., Sourina, O.: Real-time subject-dependent EEG-based emotion recognition algorithm. In: Gavrilova, M.L., Tan, C.J.K., Mao, X., Hong, L. (eds.) Transactions on Computational Science XXIII. LNCS, vol. 8490, pp. 199–223. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43790-2_11
Bradley, M.M., Lang, P.J.: The International Affective Digitized Sounds, 2nd edn., IADS-2. Affective Ratings of Sounds and Instruction Manual, University of Florida, Gainesville (2007)
Emotiv. http://www.emotiv.com
Acknowledgment
This research is supported by Singapore Maritime Institute and by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative. We would like to acknowledge the final year project students of School of MAE of Nanyang Technological University and personally Lee Jian Wei for his contribution in this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Lim, W.L. et al. (2018). EEG-Based Mental Workload and Stress Monitoring of Crew Members in Maritime Virtual Simulator. In: Gavrilova, M., Tan, C., Sourin, A. (eds) Transactions on Computational Science XXXII. Lecture Notes in Computer Science(), vol 10830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56672-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-56672-5_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-56671-8
Online ISBN: 978-3-662-56672-5
eBook Packages: Computer ScienceComputer Science (R0)