Motor imagery EEG signal classification using upper triangle filter bank auto-encode method

Abstract

In motor-imagery-based brain–computer interfaces, the frequency, and spatial information of electroencephalography signals can be used to improve the performance of motor imagery classification. However, the problem of subject-specific frequency band selection occurs frequently in spatial feature extraction. In this study, to enhance the frequency information in a spatial filter, we design an upper triangle filter bank to determine discriminative frequency components and apply the common spatial pattern to extract spatial features from sub-bands. Furthermore, an autoencoder neural network is constructed to reduce the high dimensionality of spatial features. The classification performance of the proposed method is experimentally evaluated on motor imagery datasets. The proposed method provides more discriminative features and higher classification performance in comparison with competing algorithms. This proposed filter bank method can be used to extend the other spatial and spectral processing method for motor imagery classification.