Deep convolutional neural networks and power spectral density features for motor imagery classification of EEG signals

Abstract

A Brain-Computer Interface (BCI) is a communication and control system that attempts to provide real-time interaction between a user and a computer device, based on the brain electrical signals that are generated when user imagine specific movements or actions. For doing so, classification models are developed to identify the user movement intention according to specific signal features. This paper presents a classification model to BCI that is based on the processing of Electroencephalography (EEG) signals. The power spectral density (PSD) representation of EEG signals is used for training a deep Convolutional Neural Network (CNN) that is able to differentiate among four different movement intentions: left-hand movement, right-hand movement, feet movement, and tongue movement. Performance evaluation results reported a mean accuracy of 0.8797 ± 0.0296 for the well-known BCI Competition IV Dataset 2a, which outperform state-of-the-art approaches.