Single-trial EEG classification of motor imagery using deep convolutional neural networks

Electroencephalogram (EEG) signal recorded during motor imagery (MI) has been widely applied in non-invasive brain-computer interface (BCI) as a communication approach. In this paper, we propose a new method based on the deep convolutional neural network (CNN) to perform feature extraction and classification for single-trial MI EEG. Firstly, based on the spatio-temporal characteristics of EEG, a 5-layer CNN model is built to classify MI tasks (left hand and right hand movement); then the CNN model is applied in the experimental data set collected from subjects, and compared with other three conventional classification methods (power + SVM, CSP + SVM and AR + SVM). The results demonstrate that CNN can further improve classification performance: the average accuracy using CNN (86.41 ± 0.77%) is 9.24%, 3.80% and 5.16% higher than those using power + SVM, CSP + SVM and AR + SVM, respectively. The present study shows that the proposed method is effective to classify MI, and provides a practical method by non-invasive EEG signal in BCI applications.