Hostname: page-component-848d4c4894-v5vhk Total loading time: 0 Render date: 2024-06-29T22:18:40.315Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparing EEG Brain Power of Mechanical Engineers in 3D CAD Modelling from 2D and 3D Representations

Published online by Cambridge University Press:  26 May 2022

F. Lukačević ,
S. Li ,
N. Becattini  and
S. Škec
F. Lukačević*
Affiliation:
University of Zagreb, Croatia Politecnico di Milano, Italy
S. Li
Affiliation:
Politecnico di Milano, Italy
N. Becattini
Affiliation:
Politecnico di Milano, Italy
S. Škec
Affiliation:
University of Zagreb, Croatia
*
fanika.lukacevic@fsb.hr

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Using the EEG features extracted from the EEG signals, the presented study investigates differences in the cognitive load posed on engineers while 3D CAD modelling in two different conditions, depending on the visual representations used as stimulus - a 2D and a 3D technical drawing of parts. The results indicate a higher cognitive load during the 2D drawing task. In addition, common indicators of the ongoing spatial information processing were recognised - a suppression of parietal and occipital alpha power, a higher frontal theta, and differences in theta power between the hemispheres.

Keywords

electroencephalographybrain activityvisualisationcomputer-aided design (CAD)design cognition
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 901 - 910
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), 2022.

References

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. 10.1109/TBME.2006.879459.CrossRefGoogle ScholarPubMed
Cowan, C., Girdner, J., Majdic, B. and Barrella, E.M. (2018), “Validating the use of B-Alert live electroencephalography in measuring cognitive load with the NASA Task Load Index”, American Society for Engineering Education Southeastern Section Conference, No. March, available at: https://www.researchgate.net/publication/326301439.Google Scholar
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. 10.1007/s00784-016-1902-4Google Scholar
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. 10.1016/j.jneumeth.2003.10.009.CrossRefGoogle Scholar
Fajen, B.R. and Phillips, F. (2013), “Spatial perception and action”, in Waller, D. and Nadel, L. (Eds.), Handbook of Spatial Cognition, First Edit., American Psychological Association, Washington DC, pp. 67–80. 10.1037/13936-004.Google Scholar
Galy, E., Cariou, M. and Mélan, C. (2012), “What is the relationship between mental workload factors and cognitive load types?”, International Journal of Psychophysiology, Elsevier B.V., Vol. 83 No. 3, pp. 269–275. 10.1016/j.ijpsycho.2011.09.023Google Scholar
Gerlič, I. and Jaušovec, N. (1999), “Multimedia: Differences in cognitive processes observed with EEG”, Educational Technology Research and Development, Vol. 47 No. 3, pp. 5–14. 10.1007/bf02299630Google 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. 10.1080/14639220210159717CrossRefGoogle Scholar
Goel, A.K., Vattam, S., Wiltgen, B. and Helms, M. (2012), “Cognitive, collaborative, conceptual and creative - Four characteristics of the next generation of knowledge-based CAD systems: A study in biologically inspired design”, CAD Computer Aided Design, Elsevier Ltd, Vol. 44 No. 10, pp. 879–900. 10.1016/j.ijpsycho.2011.09.023Google Scholar
Gundel, A. and Wilson, G.F. (1992), “Topographical changes in the ongoing EEG related to the difficulty of mental tasks”, Brain Topography, Vol. 5 No. 1, pp. 17–25. 10.1007/BF01129966CrossRefGoogle Scholar
Hay, L., Cash, P. and McKilligan, S. (2020), “The future of design cognition analysis”, Design Science, Vol. 6 No. 20, pp. 1–26. 10.1017/dsj.2020.20CrossRefGoogle 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. 10.1100/tsw.2009.83CrossRefGoogle ScholarPubMed
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. 10.1016/j.ijpsycho.2011.09.023CrossRefGoogle 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. 10.1017/pds.2021.77Google Scholar
Liu, Y., Ritchie, J.M., Lim, T., Kosmadoudi, Z., Sivanathan, A. and Sung, R.C.W. (2014), “A fuzzy psycho-physiological approach to enable the understanding of an engineer's affect status during CAD activities”, CAD Computer Aided Design, Elsevier Ltd, Vol. 54, pp. 19–38. 10.1016/j.cad.2013.10.007Google Scholar
Maccioni, L. and Borgianni, Y. (2020), “Review of the use of neurophysiological and biometric measures in experimental design research Review of the use of neurophysiological and biometric measures in experimental design research”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM. 10.1017/S0890060420000062Google 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, T.A. and Zeng, Y. (2010), “Analysis of design activities using EEG signals”, Proceedings of the ASME Design Engineering Technical Conference, Vol. 5 No. January 2010, pp. 277–286. 10.1115/DETC2010-28477Google Scholar
Nguyen, T.A. and Zeng, Y. (2014), “A physiological study of relationship between designer's mental effort and mental stress during conceptual design”, CAD Computer Aided Design, Elsevier Ltd, Vol. 54, pp. 3–18.Google Scholar
Peirce, J.W., Gray, J.R., Simpson, S., MacAskill, M. R., Höchenberger, R., Sogo, H. and Kastman, E., Lindeløv, J. (2019), “PsychoPy2: Experiments in behavior made easy”, Behavior Research Methods, 10.3758/s13428-018-01193-y.CrossRefGoogle Scholar
Rosso, P., Gopsil, J., Hicks, B. and Burgess, S. (2020), “Investigating and characterising variability in CAD modelling: An overview”, Proceedings of CAD’20, pp. 226–230. 10.14733/cadconfp.2020.226-230CrossRefGoogle Scholar
Rugg, M.D. and Dickens, A.M.J. (1982), “Dissociation of alpha and theta activity as a function of verbal and visuospatial tasks”, Electroencephalography and Clinical Neurophysiology, Vol. 53, pp. 201–207. 10.1016/0013-4694(82)90024-4CrossRefGoogle ScholarPubMed
Vieira, S., Gero, J.S., Delmoral, J., Gattol, V., Fernandes, C., Parente, M. and Fernandes, A.A. (2020), “The neurophysiological activations of mechanical engineers and industrial designers while designing and problem-solving”, Design Science, Vol. 6 No. September 2018, pp. 1–35. 10.1017/dsj.2020.26CrossRefGoogle Scholar
Willis, S.G., Wheatley, G.H. and Mitchell, O.R. (1979), “Cerebral processing of spatial and verbal-analytic tasks: An EEG study”, Neuropsychologia, Vol. 17 No. 5, pp. 473–484. 10.1016/0028-3932(79)90054-XCrossRefGoogle ScholarPubMed