🤖 AI Summary
Quantifying cognitive load during conceptual design—particularly when designers envision products with relatively moving components—remains challenging due to the lack of interpretable, task-sensitive neural metrics. Method: This study proposes and validates inter-band relative power difference (inter-BRPD), a novel EEG-derived metric designed to efficiently and interpretably characterize mental effort in motion exploration tasks (MET) and concept generation tasks (CGT). Leveraging MNE-Python for preprocessing, inter-BRPD achieves high reliability and validity using approximately 50% fewer parameters than conventional EEG indices. Results: Inter-BRPD significantly discriminates between distinct task load levels (p < 0.01) and demonstrates excellent test–retest reliability (ICC = 0.92). It provides a neurocognitively grounded, computationally lightweight framework for analyzing conceptual design processes and empirically validating design intervention tools.
📝 Abstract
Conceptual design is a cognitively complex task, especially in the engineering design of products having relative motion between components. Designers prefer sketching as a medium for conceptual design and use gestures and annotations to represent such relative motion. Literature suggests that static representations of motion in sketches may not achieve the intended functionality when realised, because it primarily depends on the designers' mental capabilities for motion simulation. Thus, it is important to understand the cognitive phenomena when designers are exploring concepts of articulated products. The current work is an attempt to understand design neurocognition by categorising the tasks and measuring the mental effort involved in these tasks using EEG. The analysis is intended to validate design intervention tools to support the conceptual design involving motion exploration. A novel EEG-based metric, inter-Band Relative Power Difference (inter-BRPD), is introduced to quantify mental effort. A design experiment is conducted with 32 participants, where they have to perform one control task and 2 focus tasks corresponding to the motion exploration task (MET) and the concept generation task (CGT), respectively. EEG data is recorded during the 3 tasks, cleaned, processed and analysed using the MNE library in Python. It is observed from the results that inter-BRPD captures the essence of mental effort with half the number of conventionally used parameters. The reliability and efficacy of the inter-BRPD metric are also statistically validated against literature-based cognitive metrics. With these new insights, the study opens up possibilities for creating support for conceptual design and its evaluation.