Hostname: page-component-f554764f5-8cg97 Total loading time: 0 Render date: 2025-04-12T08:28:53.279Z Has data issue: false hasContentIssue false
  • English
  • Français

CORRELATING DESIGN PERFORMANCE TO EEG ACTIVATION: EARLY EVIDENCE FROM EXPERIMENTAL DATA

Published online by Cambridge University Press:  27 July 2021

Shumin Li ,
Niccolò Becattini  and
Gaetano Cascini
Shumin Li*
Affiliation:
Politecnico di Milano
Niccolò Becattini
Affiliation:
Politecnico di Milano
Gaetano Cascini
Affiliation:
Politecnico di Milano
*
Li, Shumin, Politecnico di Milano, Mechanical Engineering, Italy, [email protected]

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.

This paper presents an EEG (Electroencephalography) study that explores correlations between the neurophysiological activations, the nature of the design task and its outputs. We propose an experimental protocol that covers several design-related tasks: including fundamental activities (e.g. idea generation and problem-solving) as well as more comprehensive task requiring the complex higher-level reasoning of designing. We clustered the collected data according to the characteristics of the design outcome and measured EEG alpha band activation during elementary and higher-level design task, whereas just the former yielded statistically significant different behaviour in the left frontal and occipital area. We also found a significant correlation between the ratings for elementary sketching task outcomes and EEG activation at the higher-level design task. These results suggested that EEG activation enables distinguishing groups according to their performance only for elementary tasks. However, this also suggests a potential application of EEG data on the elementary tasks to distinguish the designers' brain response during higher-level of design task.

Keywords

ElectroencephalographyNeurocognitionHuman behaviour in designCreativityDesign cognition
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 771 - 780
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), 2021. Published by Cambridge University Press

References

Abraham, A. (2018), The Neuroscience of Creativity, Cambridge University Press,10.1017/9781316816981CrossRefGoogle Scholar
Alexiou, K., Zamenopoulos, T. and Gilbert, S. (2011), “Imaging the Designing Brain: A Neurocognitive Exploration of Design Thinking”, Design Computing and Cognition ’10, Dordrecht, pp. 489–504,CrossRefGoogle Scholar
Appelbaum, L.G., Ales, J.M., Cottereau, B. and Norcia, A.M. (2010), “Configural specificity of the lateral occipital cortex”, Neuropsychologia, Vol. 48 No. 11, pp. 3323–3328,10.1016/j.neuropsychologia.2010.07.016CrossRefGoogle ScholarPubMed
Arden, R., Chavez, R.S., Grazioplene, R. and Jung, R.E. (2010), “Neuroimaging creativity: A psychometric view”, Behavioural Brain Research, Vol. 214 No. 2, pp. 143–156,CrossRefGoogle ScholarPubMed
Benedek, M., Jung, R.E. and Vartanian, O. (2018), “The neural bases of creativity and intelligence: common ground and differences”, Neuropsychologia, Vol. 118, pp. 1–3,10.1016/j.neuropsychologia.2018.09.006CrossRefGoogle ScholarPubMed
Bhattacharya, J. and Petsche, H. (2005), “Drawing on mind's canvas: Differences in cortical integration patterns between artists and non-artists”, Human Brain Mapping, Vol. 26 No. 1, pp. 1–14,10.1002/hbm.20104CrossRefGoogle ScholarPubMed
Borga, M. and Knutsson, H. (2001), A Canonical Correlation Approach to Blind Source Separation, SE-581 85 Linköping, Sweden.Google Scholar
Chávez, R.A., Graff-Guerrero, A., Garcia-reyna, J., Vaugier, V., et al. (2004), “Neurobiology of creativity: Preliminary results from a brain activation study”, Salud Mental, Vol. 27 No. 3, pp. 38–46.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. 2583–2587,10.1109/TBME.2006.879459CrossRefGoogle 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. 134 No. 1, pp. 9–21CrossRefGoogle ScholarPubMed
Fink, A., Graif, B. and Neubauer, A.C. (2009), “Brain correlates underlying creative thinking: EEG alpha activity in professional vs. novice dancers”, NeuroImage, Vol. 46 No. 3, pp. 854–862,CrossRefGoogle ScholarPubMed
Foxe, J. and Snyder, A. (2011), “The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention”, Frontiers in Psychology, Vol. 2, p. 15410.3389/fpsyg.2011.00154CrossRefGoogle ScholarPubMed
Gibson, C., Folley, B.S. and Park, S. (2009), “Enhanced divergent thinking and creativity in musicians: A behavioral and near-infrared spectroscopy study”, Brain and Cognition, Vol. 69 No. 1, pp. 162–169,10.1016/j.bandc.2008.07.009CrossRefGoogle ScholarPubMed
Göker, M.H. (1997), “The effects of experience during design problem solving”, Design Studies, Vol. 18 No. 4, pp. 405–426, https://doi.org/10.1016/s0142-694x(97)00009-4.CrossRefGoogle Scholar
Guilford, J.P. (1967), The Nature of Human Intelligence, McGraw-Hill, New York.Google Scholar
Heaton, R.K., Chelune, G.J., Talley, J.L., Kay, G.G. and Curtiss, G. (1993), Wisconsin Card Sorting Test Manual: Revised and Expanded, Psychological Assessment Resources Inc, Odessa, FL.Google Scholar
Jackson, A.F. and Bolger, D.J. (2014), “The neurophysiological bases of EEG and EEG measurement: A review for the rest of us”, Psychophysiology, Vol. 51 No. 11, pp. 1061–1071CrossRefGoogle Scholar
Jiang, X., Bian, G.-B. and Tian, Z. (2019), “Removal of Artifacts from EEG Signals: A Review”, Sensors, Vol. 19 No. 5, https://doi.org/10.3390/s19050987.Google ScholarPubMed
Kirchner, W.K. (1958), “Age differences in short-term retention of rapidly changing information”, Journal of Experimental Psychology, Vol. 55 No. 4, pp. 352–358, https://doi.org/10.1037/h0043688.CrossRefGoogle ScholarPubMed
Klimesch, W., Doppelmayr, M., Schwaiger, J., Auinger, P. and Winkler, T. (1999), “‘Paradoxical’ alpha synchronization in a memory task”, Cognitive Brain Research, Vol. 7 No. 4, pp. 493–501,CrossRefGoogle Scholar
Knyazev, G.G. (2007), “Motivation, emotion, and their inhibitory control mirrored in brain oscillations”, Neuroscience and Biobehavioral Reviews, Vol. 31 No. 3, pp. 377–395,CrossRefGoogle ScholarPubMed
Kounios, J. and Beeman, M. (2014), “The Cognitive Neuroscience of Insight”, Annual Review of Psychology, Vol. 65 No. 1, pp. 71–93, https://doi.org/10.1146/annurev-psych-010213-115154.CrossRefGoogle ScholarPubMed
Martindale, C. and Mines, D. (1975), “Creativity and cortical activation during creative, intellectual and EEG feedback tasks”, Biological Psychology, Vol. 3 No. 2, pp. 91–100,10.1016/0301-0511(75)90011-3CrossRefGoogle ScholarPubMed
Mednick, S.A. (1968), “The Remote Associates Test”, The Journal of Creative Behavior, Vol. 2 No. 3, pp. 213–214, https://doi.org/https://doi.org/10.1002/j.2162-6057.1968.tb00104.x.CrossRefGoogle Scholar
Nguyen, T.A. and Zeng, Y. (2010), “Analysis of Design Activities Using EEG Signals”, ASME Design Engineering Technical Conference, Vol. 5, pp. 277–286, https://doi.org/10.1115/DETC2010-28477.Google Scholar
Peirce, J., Gray, J.R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., et al. (2019), “PsychoPy2: Experiments in behavior made easy”, Behavior Research Methods, Vol. 51 No. 1, pp. 195–203, https://doi.org/10.3758/s13428-018-01193-y.CrossRefGoogle ScholarPubMed
Privodnova, E.Y., Volf, N. V. and Knyazev, G.G. (2017), “Specific features of the oscillatory brain activity during the final stage of creative problem solving in young and elderly subjects”, Human Physiology, Vol. 43 No. 3, pp. 241–247, https://doi.org/10.1134/S0362119717030161.CrossRefGoogle Scholar
Razumnikova, O.M. (2004), “Gender differences in hemispheric organization during divergent thinking: An EEG investigation in human subjects”, Neuroscience Letters, Vol. 362 No. 3, pp. 193–195,10.1016/j.neulet.2004.02.066CrossRefGoogle ScholarPubMed
Smith, M.E., Gevins, A., Brown, H., Karnik, A. and Du, R. (2001), “Monitoring Task Loading with Multivariate EEG Measures during Complex Forms of Human-Computer Interaction”, Human Factors, SAGE Publications Inc, Vol. 43 No. 3, pp. 366–380, https://doi.org/10.1518/001872001775898287.Google Scholar
Torrance, E.P. (1968), Torrance Tests of Creative Thinking, Personnel Press, Princeton, N.J.Google Scholar
Vieira, S., Gero, J.S., Delmoral, J., Li, S., Cascini, G. and Fernandes, A. (2020), “Brain Activity in Constrained and Open Design Spaces: an EEG Study”, ICDC, pp. 68–75, https://doi.org/10.35199/icdc.2020.09.Google Scholar
Vos, D.M., Riès, S., Vanderperren, K., Vanrumste, B., Alario, F.-X., Huffel, V.S. and Burle, B. (2010), “Removal of Muscle Artifacts from EEG Recordings of Spoken Language Production”, Neuroinformatics, Vol. 8 No. 2, pp. 135–150, https://doi.org/10.1007/s12021-010-9071-0.CrossRefGoogle ScholarPubMed