Affiliation:
1. School of Mechanical Engineering Beijing Institute of Technology Beijing China
2. Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing China
3. Key Laboratory of Industry Knowledge & Data Fusion Technology and Application, Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China
4. School of Mechatronical Engineering Beijing Institute of Technology Beijing China
Abstract
AbstractThe past two decades have witnessed dramatic advancement in computer‐aided design (CAD). However, development of human–computer interfaces (HCI) for CAD have not kept up with these advances. Windows, Icons, Menus, Pointer (WIMP) is still the mainly used interface for CAD applications which limits the naturalness and intuitiveness of the CAD modeling process. As a novel interface, Brain–computer interfaces (BCIs) have great potential in the application of CAD modeling. Utilizing BCIs, the user can create CAD models just by thinking about it in principle, because BCIs provide an end‐to‐end interaction channel between users and CAD models. However, current related studies are mainly limited to the existing BCIs paradigms, while ignoring the relationship between electroencephalogram (EEG) signals and CAD models, which largely increases the cognitive load on the users. In this study, we aimed to explore the potential of using BCI to create CAD models directly independent of the classical BCIs paradigms. For this purpose, EEG signals evoked by six basic CAD models (i.e., point, square, trapezoid, line, triangle, and circle) were collected from 28 participants. After preprocessing and sub‐trial principal components analysis (st‐PCA) of recorded data, the peak, mean and time‐frequency energy features were extracted from EEG signals. By applying the one‐way repeated measures analysis of variance, we demonstrated that there were significant differences among these EEG features evoked by different CAD models. These features from EEG electrode channels ranked by mutual information were then used to train a discriminant classifier of genetic algorithm‐based support vector machine. The empirical result showed that this classifier can discriminate the CAD models with an average accuracy of about 72%, which turns out that EEG based model generation is feasible, and provides the technical and theoretical basis for building a novel BCI for CAD modeling.