Abstract

Info

Title:: Motor imagery EEG classification and recognition based on differential entropy and convolutional neural network

Author(s):: LIAN Xiaoqin1; 2; CAI Mohao1; 2; GAO Chao1; 2; LUO Zhihong1; 2; WU Yelan1; 2; 1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China 2. Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China

Keywords:: Keywords: motor imagery EEG signal convolutional neural network differential entropy feature extraction

Abstract:: Abstract: To address the problem of low accuracy in multi-classification recognition of motor imagery electroencephalogram (EEG) signals, a recognition method is proposed based on differential entropy and convolutional neural network for 4-class classification of motor imagery. EEG signals are extracted into 4 frequency bands (Alpha, Beta, Theta, and Gamma) through the filter, followed by the computation of differential entropy for each frequency band. According to the spatial characteristics of brain electrodes, the data structure is reconstructed into three-dimensional EEG signal feature cube which is input into convolutional neural network for 4-class classification. The method achieves an accuracy of 95.88% on the BCI Competition IV-2a public dataset. Additionally, a 4-class classification motor imagery dataset is established in the laboratory for the same processing, and an accuracy of 94.50% is obtained. The test results demonstrate that the proposed method exhibits superior recognition performance.