A hybrid SAE and CNN classifier for motor imagery EEG classification

Abstract

The research of EEG classification is of great significance to the application and development of brain-computer interface. The realization of brain-computer interface depends on the good accuracy and robustness of EEG classification. Because the brain electrical capacitance is susceptible to the interference of noise and other signal sources (EMG, EEG, ECG, etc.), EEG classifier is difficult to improve the accuracy and has very low generalization ability. A novel method based on sparse autoencoder (SAE) and convolutional neural network (CNN) is proposed for feature extraction and classification of motor imagery electroencephalogram (EEG) signals. The performance of the proposed method is evaluated with real EEG signals from different subjects. The experimental results show that the network structure can get better classification results than other classification algorithms.