Studying The Neurophysiology of Designing Through an EEG Study of Layout Design: Preliminary Results

The study described in this workshop paper is part of a larger research project whose goal is to correlate design cognition with brain behavior. This paper presents preliminary results from an experiment using EEG to measure brain activation to study design cognition. In this study, we adopted and then extended the tasks described in a previous fMRI study of design cognition reported in the literature. The block experiment consists of a sequence of 3 tasks: problem solving, basic design and open design using a physical interface. The block is preceded by a familiarizing pre-task using the physical interface and then extended to a fourth task using free-hand sketching. We have collected brainwaves in experiments with 44 participants with backgrounds in industrial design, architecture, engineering design and graphic design. This paper shows preliminary neurophysiological results from 12 participants, comparing industrial designers and mechanical engineers. Preliminary results indicate task-related power differences in activation between the problemsolving task and the design tasks, differences in band power levels and temporal resolution across participants and across backgrounds. The results from this study will contribute to t h e correlation of brain behavior during designing with design cognition. 1. BACKGROUND The understanding of the neurocognitive processes of design is still in its infancy. Design cognition functional magnetic resonance imaging (fMRI)-based studies are at the exploratory stage with one wellcontrolled experiment published (Alexiou et al. 2009). The application of electroencephalography (EEG) and the development of readily available software as a research tool has increased, partly due the reduction in the cost of the equipment. This has made the collection and analysis of EEG signals a viable option for design researchers. Design cognition EEG-based studies are also at an exploratory stage with only a few reported studies on engineering design and architectural design. Results from controlled experiments identify EEG bands associated with the design activities of problem solving and evaluation (Liu et al, 2016), neurophysiological EEG signals to study effort, fatigue and concentration in conceptual design (Nguyen et al. 2016), and neurophysiological correlates of embodiment and motivational factors during the perception of virtual architecture (Vecchiato et al. 2015). Results open the way for researching the distinctive nature of design. The use of EEG allows the precise measurement of rapid changes in neural activity related to observable stimuli, making it possible to assess the timing of task-relevant cognitive operations and to separate component processes in the stream of information processing (Stern, Ray and Quigley, 2001). EEG can provide high temporal resolution during cognition that can help elucidate the stages of the participant recognition of tasks and provide the temporal basis for information processing (Hinterberger et al. 2014). Averaging the measurements yields a measure of the EEG voltages that are consistently related to the sensory, perceptual and decision-making processes that followed the stimulus in the task experiment (Dickter and Kieffaber, 2014). In the field of psychology, the study of creativity (Fink et al. 2009a, Fink et al. 2009b) has advanced to time-related neural responses during the process of creative ideation (i.e., the generation of creative ideas) and to the patterns of EEG alpha power and its changes (Schwab et al. 2014; Rominger et al. 2018). However time-related neural responses during problem solving compared to design tasks are as yet unknown. Studies of insights based on neuroscience techniques are valuable for design research as the higher activation of the dorsolateral prefrontal cortex is consistent for design tasks and illstructured problems (Alexiou et al. 2009), and insight (Kounios and Beeman 2009, Beeman et al. 2004), and recruits a more extensive network of brain areas than problem solving (Alexiou et al. 2009, Kounios