Artificial and Convolutional Neural Network of EEG-Based Motor imagery classification: A Comparative Study

Electroencephalography (EEG) signal recorded during motor imagery (MI) has been frequently used in noninvasive Brain-Computer Interface (BCI) is a new type of device that allows direct communication between user's brain and machine. This paper proposes a novel solution for extraction and classification of left/right hand, both feet, and tongue movement by exploiting two approaches of deep learning such as artificial neural network ANN and convolutional neural network CNN. A wide range of spatial and frequency domain features are extracted from the EEG signals and to train an ANN and CNN networks to perform the classification tasks. The EEG signals of mental tasks are extracted and classified by these architectures. In addition, the proposed methods are validated by the EEG dataset of the BCI competition IV-2a and we compared them with each other. The results show that the CNN model surpasses the ANN model by an accuracy value of 60.55%.