• Publisher: Purdue University
  • Authors: Wan-Lin Hu, Joran W. Booth, and Tahira Reid


Using electroencephalography (EEG) to predict design outcomes could be used in many applications as it facilitates the correlation of engagement and cognitive workload with ideation effectiveness. It also establishes a basis for the connection between EEG measurements and common constructs in engineering design research. In this paper, we propose a support vector machine (SVM)-based prediction model for design outcomes using EEG metrics and some demographic factors as predictors. We trained and validated the model with more than 100 concepts, and then evaluated the relationship between EEG data and concept-level measures of novelty, quality, and elaboration. The results characterize the combination of engagement and workload that is correlated with good design outcomes. Findings also suggest that EEG technologies can be used to partially replace or augment traditional ideation metrics and to improve the efficacy of ideation research.


  • Design
  • Performance
  • Electroencephalography
  • Support vector machines