EEG-based cognitive load indicators in CAD modelling tasks of varying complexity
Lukačević, Fanika (1,2); Becattini, Niccolò (2); Škec, Stanko (1)
Type:
Year:
2023
Editor:
Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad
Author:
Series:
ICED
Institution:
1: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture;
2: Politecnico di Milano, Department of Mechanical Engineering
Section:
Design Methods
Page(s):
1545-1554
DOI number:
Abstract:
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:
