Motor imagery EEG classification and recognition based on differential entropy and convolutional neural network
10.3969/j.issn.1005-202X.2024.03.016
- VernacularTitle:基于微分熵及卷积神经网络的脑电运动想象分类识别
- Author:
Xiaoqin LIAN
1
,
2
;
Mohao CAI
;
Chao GAO
;
Zhihong LUO
;
Yelan WU
Author Information
1. 北京工商大学人工智能学院,北京 100048
2. 北京工商大学中国轻工业工业互联网与大数据重点实验室,北京 100048
- Keywords:
motor imagery EEG signal;
convolutional neural network;
differential entropy;
feature extraction
- From:
Chinese Journal of Medical Physics
2024;41(3):375-381
- CountryChina
- Language:Chinese
-
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.
