Classification of user competency levels using EEG and convolutional neural network in 3D modelling application

Competency classification is one of the main challenging tasks for the development of state-of-the-art next generation computer-aided design (CAD) system. To develop a futuristic system that can accommodate the lack of competency, the system needs to adapt to the competency level of the user. To solve this problem, we have presented a deep convolutional neural network (CNN) model that uses the Electroencephalography (EEG) of the user to classify the level of competency in 3D modeling task. The five competency levels were defined based on the task completion time, final 3D model rating and previous modeling experience. This is the first study that classifies user competency and employs the CNN model for the analysis of EEG signals in the design application. In this work, a 14-layer deep CNN model was implemented to classify competency into five different levels. The proposed technique achieved an accuracy, specificity, and sensitivity of > 88%, > 90% and > 70% respectively with 5-fold cross-validation. The results showed the applicability of a CNN model to classify the user competency and can be used as a first step in developing state-of-the-art adaptive 3D modeling systems.