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EEG-BASED COGNITIVE LOAD INDICATORS IN CAD MODELLING TASKS OF VARYING COMPLEXITY

Published online by Cambridge University Press:  19 June 2023

Fanika Lukačević ,
Niccolò Becattini  and
Stanko Škec
Fanika Lukačević*
Affiliation:
University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture Politecnico di Milano, Department of Mechanical Engineering
Niccolò Becattini
Affiliation:
Politecnico di Milano, Department of Mechanical Engineering
Stanko Škec
Affiliation:
University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture
*
Lukacevic, Fanika, University of Zagreb. Faculty of Mechanical Engineering and Naval Architecture, Croatia, fanika.lukacevic@fsb.hr

Abstract

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As an initial step towards a better understanding of cognitive load in computer-aided design (CAD), the herein presented study investigated cognitive load imposed on 24 mechanical engineers during two CAD modelling tasks of intentionally different complexity levels. The cognitive load has been rarely studied in the CAD context, which resulted in the lack of understanding if and how the EEG-based indicators available from the literature reflect the changes in cognitive load imposed on engineering designers in CAD activities. Therefore, cognitive load was measured and analysed using three EEG-based indicators to explore insights that might be obtained from them. The initial analysis revealed different cognitive load results from the employed indicators for the same EEG data. In addition, the study implies that the cognitive load results obtained through the used indicators are only partially coherent with the CAD modelling task complexity. Hence, the results imply that the chosen EEG-based indicator matters when measuring and analysing cognitive load in CAD modelling tasks and that its adjustment for CAD context might be needed.

Keywords

Computer Aided Design (CAD)Cognitive loadElectroencephalographyComplexityDesign cognition
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1545 - 1554
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Antonenko, P., Paas, F., Grabner, R. and van Gog, T. (2010), “Using Electroencephalography to Measure Cognitive Load”, Educational Psychology Review, Vol. 22 No. 4, pp. 425–438.CrossRefGoogle Scholar
Balters, S., Weinstein, T., Mayseless, N., Medicine, S. and Auernhammer, J. (2022), “Design science and neuroscience : A systematic review of the emergent field of design neurocognition”, Design Studies, No. November.CrossRefGoogle Scholar
Barrella, E.M., Cowan, C., Girdner, J., Watson, M.K. and Anderson, R. (2019), “Measuring connections: Engineering students’ cognitive activities and performance on complex tasks”, Proceedings - Frontiers in Education Conference, FIE, Vol. 2019-Octob, IEEE, available at:https://doi.org/10.1109/FIE43999.2019.9028595.Google Scholar
De Clercq, W., Vergult, A., Vanrumste, B., Van Paesschen, W. and Van Huffel, S. (2006), “Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram”, IEEE Transactions on Biomedical Engineering, Vol. 53 No. 12, pp. 2583–2587.CrossRefGoogle ScholarPubMed
Dan, A. and Reiner, M. (2017), “EEG-based cognitive load of processing events in 3D virtual worlds is lower than processing events in 2D displays”, International Journal of Psychophysiology, Elsevier B.V., Vol. 122, pp. 75–84.CrossRefGoogle ScholarPubMed
Delorme, A. and Makeig, S. (2004), “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis”, J Neurosci Methods, Vol. Mar 15 No. 134(1), pp. 9–21.CrossRefGoogle Scholar
Gevins, A. and Smith, M.E. (2003), “Neurophysiological measures of cognitive workload during human-computer interaction”, Theoretical Issues in Ergonomics Science, Vol. 4 No. 1–2, pp. 113–131.CrossRefGoogle Scholar
Holm, A., Lukander, K., Korpela, J., Sallinen, M. and Müller, K.M.I. (2009), “Estimating brain load from the EEG”, TheScientificWorldJournal, Vol. 9, pp. 639–651.CrossRefGoogle ScholarPubMed
Jia, W., von Wegner, F., Zhao, M. and Zeng, Y. (2021), “Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks”, Scientific Reports, Nature Publishing Group UK, Vol. 11 No. 1, pp. 1–24.Google ScholarPubMed
Johnson, M.D., Valverde, L.M. and Thomison, W.D. (2018), “An investigation and evaluation of computer-aided design model complexity metrics”, Computer-Aided Design and Applications, Vol. 15 No. 1, pp. 61–75.CrossRefGoogle Scholar
Klimesch, W., Schack, B. and Sauseng, P. (2005), “The functional significance of theta and upper alpha oscillations”, Experimental Psychology, Vol. 52 No. 2, pp. 99–108.CrossRefGoogle ScholarPubMed
Li, S., Becattini, N. and Cascini, G. (2021), “Correlating design performance to EEG activation: Early evidence from experiental data”, Proceedings of the Design Society, pp. 771–780.CrossRefGoogle Scholar
Maier, A.M., Baltsen, N., Christoffersen, H. and Störrle, H. (2014), “Towards Diagram Understanding: A Pilot-Study Measuring Cognitive Workload Through Eye-Tracking”, Proc. Intl. Conf. Human Behavior in Design, No. October, pp. 1–6.Google Scholar
Majdic, B., Cowan, C., Girdner, J., Opoku, W., Pierrakos, O. and Barrella, E. (2017), “Monitoring Brain Waves in an Effort to Investigate Student ’ s Cognitive Load During a Variety of Problem Solving Scenarios”, Systems and Information Engineering Design Symposium (SIEDS), IEEE, Charlottesville, VA, USA, pp. 186–191.Google Scholar
McMahon, C. (2015), “Design Informatics: Supporting Engineering Design Processes with Information Technology”, Journal of the Indian Institute of Science, Vol. 95 No. 4, pp. 365–377.Google Scholar
Nguyen, P., Nguyen, T.A. and Zeng, Y. (2018), “Empirical approaches to quantifying effort, fatigue and concentration in the conceptual design process: An EEG study”, Research in Engineering Design, Springer London, Vol. 29 No. 3, pp. 393–409.CrossRefGoogle Scholar
Nolte, H. and McComb, C. (2021), “The cognitive experience of engineering design: an examination of first-year student stress across principal activities of the engineering design process”, Design Science, Vol. 7 No. May, available at:https://doi.org/10.1017/dsj.2020.32.CrossRefGoogle Scholar
Pfurtscheller, G. and da Silva, Lopes, F.H. (1999), “Event-related EEG/MEG synchronization and desynchronization: basic principles”, Clinical Neurophysiology, Vol. 110 No. 11, pp. 1842–1857.CrossRefGoogle ScholarPubMed
Robertson, D., Ulrich, K. and Filerman, M. (1991), Cognitive Complexity and CAD Systems: Beyond the Drafting Board Metaphor, No. WP #3244-91-MSA, Cambridge, Massachusetts.CrossRefGoogle Scholar
Shealy, T., Gero, J., Hu, M. and Milovanovic, J. (2020), “Concept generation techniques change patterns of brain activation during engineering design”, Design Science, pp. 1–27.CrossRefGoogle Scholar
Sivanathan, A., Lim, T., Ritchie, J., Sung, R., Kosmadoudi, Z. and Liu, Y. (2015), “The application of ubiquitous multimodal synchronous data capture in CAD”, CAD Computer Aided Design, Vol. 59, pp. 176–191.CrossRefGoogle Scholar
Sweller, J., Ayres, P. and Kalyuga, S. (2011), Cognitive Load Theory, edited by Spector, J.M. and Lajoie, S.P. Springer Science+Business Media, LLC 2013, Springer, available at: http://www.springer.com/series/8640.Google Scholar
Ullman, D.G. (2002), “Toward the ideal mechanical engineering design support system”, Research in Engineering Design - Theory, Applications, and Concurrent Engineering, Vol. 13 No. 2, pp. 55–64.Google Scholar
Zhu, Y., Wang, Q. and Zhang, L. (2021), “Study of EEG characteristics while solving scientific problems with different mental effort”, Scientific Reports, Nature Publishing Group UK, Vol. 11 No. 1, pp. 1–12.CrossRefGoogle ScholarPubMed