Abstract

Recently, physiological signals such as eye-tracking and gesture analysis, galvanic skin response (GSR), electrocardiograms (ECG) and electroencephalograms (EEG) have been used by design researchers to extract significant information to describe the conceptual design process. We study a set of video-based design protocols recorded on subjects performing design tasks on a sketchpad while having their EEG monitored. The conceptual design process is rich with information on how designer’s do design. Many methods exist to analyze the conceptual design process, the most popular one being concurrent verbal protocols. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Also, verbal protocols are known to fail in some circumstances such as when dealing with creativity, insight (e.g. Aha! experience, gestalt), concurrent, nonverbalizable (e.g. facial recognition) and nonconscious processes. We propose different approaches to study the conceptual design process using electroencephalograms (EEG). More specifically, we use spatio-temporal and frequency domain features. Our research is based on machine learning techniques used on EEG signals (functional microstate analysis), source localization (LORETA) and on a novel method of segmentation for design protocols based on EEG features. Using these techniques, we measure mental effort, fatigue and concentration in the conceptual design process, in addition to creativity and insight/nonverbalizable processing. We discuss the strengths and weaknesses of such approaches.