Classification of User Competency Levels using EEG and Convolutional Neural Network in 3D Modelling Application Abstract   Competency classification is one of the main challenging tasks for the development of stateoftheart next generation computeraided 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 14layer deep CNN model was implemented to classify competency into five different levels. The proposed technique achieved an accuracy, specificity, and sensitivity of xe2x80xafu003exe2x80xaf88%, xe2x80xafu003exe2x80xaf90% and xe2x80xafu003exe2x80xaf70% respectively with 5fold crossvalidation. The results showed the applicability of a CNN model to classify the user competency and can be used as a first step in developing stateoftheart adaptive 3D modeling systems.

Shorttimespan EEGbased personalized emotion recognition with deep convolutional neural network Emotion recognition can be useful in various applications such as in neurofeedback training for functional enhancement. A practically realizable emotion recognition system should rely on as little physiological signalsmodalities as possible. Also, emotionrelated neurological activities may be vastly different from person to person. Hence, this paper presents the singlemodal EEGbased personalized emotion recognition convolutional neural network (CNN) models working on the DEAP dataset. The valence and arousal level classification performance of our presented CNN classifiers have surpassed the other emotion classifiers working on the DEAP dataset based on our scope of literature reviewed. The models, which are deep CNN, rely on only plain EEG data and require no preextracted EEG features. The design and application of the CNN models is aimed at possible future work of identification of new emotionrelated EEG features, relying on the automated feature extraction capability of the CNN. The two CNN models presented have achieved the 3class valence classification test accuracy of 97.59% and 98.75% respectively, and the 3class arousal classification test accuracy of 98.48% and 97.58%.