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

作者：

Highlights：

• Classification of user expertise level using EEG signals in a 3D modelling task.

• Convolutional neural network has been used for classification of EEG signals.

• A classification accuracy of >88% was achieved with 5-fold-cross validation.

• The method can be used in adaptive games and adaptive learning technologies.

摘要

•Classification of user expertise level using EEG signals in a 3D modelling task.•Convolutional neural network has been used for classification of EEG signals.•A classification accuracy of >88% was achieved with 5-fold-cross validation.•The method can be used in adaptive games and adaptive learning technologies.

论文关键词：Deep neural networks,EEG,Competency,CNN,Novice,Entropy

论文评审过程：Received 19 April 2019, Revised 13 August 2019, Accepted 11 January 2020, Available online 13 January 2020, Version of Record 23 January 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113202