A Convolution Neural Network with Mixed-Size Kernels for Time-Frequency Characteristics of Motor Imagery

Abstract

As a paradigm of spontaneous Brain-Computer Interface (BCI), motor imagery electroencephalogram (EEG) has always been a hot topic in BCI and clinical rehabilitation. Many algorithms have been proposed for decoding the motor imagery signals. The algorithm based on the convolutional neural network has shown excellent potential in the task of motor imagery signal classification. However, the existing models are not specific to the characteristics of motor imagery signals, and so, they cannot fully extract the signal features of different rhythms. Moreover, limited by difficulties in the acquisition, the classification effect of the model is network based on mixed-size convolution kernel is designed. The time-frequency graph obtained by Short-time Fourier transform (STFT) is used as input, and Deep Convolutional Generative Adversarial Networks (DCGANs) is used for data enhancement. The results show that the average classification accuracy is 85.7%. Compared with current mainstream classification algorithms, the model presented in this paper has shown high classification accuracy and good robustness.