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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, [email protected]

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

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