Abstract

The objective of this thesis is to use principal component analysis (PCA) to explore the relationship between neurological brain power and activities in conceptual design. This thesis provides an objective method to measure and understand designer’s activities with respect to brain signal patterns. Understanding designer’s activities may help us develop powerful tools to improve designer’s performance. This thesis is based on the cognitive experiments consisting of 6 design tasks conducted at the Concordia Design Lab (Nguyen & Zeng, 2016).
First, we observed the electroencephalogram (EEG) data of closed eyes rest states and design activities (synthesis and evaluation) using statistical methods. We found that the 7 bands of subjects’ EEG power are normally distributed. Then we averaged the 32 subjects’ relative EEG band power, we found that alpha band power negatively correlated to the other band powers.
Second, we applied PCA to the data. We found that there are three principal components (PCs) that account for most of the variance (97%) of the EEG band power. With respect to the results of 3 PCs, we found that the rest segments are significantly different from the design activity segments, synthesis segments have greater variance than evaluating solution segments, and they are not significantly related. From the results of 3PC, we may observe the EEG data as the baseline of design activities.
Third, by comparing the differences of the subjects on the PCs, we might infer or evaluate the subject’s design behavior. By optimizing the model, ultimately it may help us improve the performance of design.