[1]廉小亲,蔡沫豪,高超,等.基于微分熵及卷积神经网络的脑电运动想象分类识别[J].中国医学物理学杂志,2024,41(3):375-381.[doi:DOI:10.3969/j.issn.1005-202X.2024.03.016]
　LIAN Xiaoqin,CAI Mohao,et al.Motor imagery EEG classification and recognition based on differential entropy and convolutional neural network[J].Chinese Journal of Medical Physics,2024,41(3):375-381.[doi:DOI:10.3969/j.issn.1005-202X.2024.03.016]
点击复制

基于微分熵及卷积神经网络的脑电运动想象分类识别()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第3期
页码:
375-381
栏目:
医学信号处理与医学仪器
出版日期:
2024-03-27

文章信息/Info

Title:
Motor imagery EEG classification and recognition based on differential entropy and convolutional neural network
文章编号:
1005-202X（2024）03-0375-07
作者:
廉小亲1; 2; 蔡沫豪1; 2; 高超1; 2; 罗志宏1; 2; 吴叶兰1; 2
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
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
关键词:
运动想象脑电信号; 卷积神经网络; 微分熵; 特征提取
Keywords:
Keywords: motor imagery EEG signal convolutional neural network differential entropy feature extraction
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2024.03.016
文献标志码:
A
摘要:
针对基于运动想象的脑电信号多分类识别准确率不高的问题，提出一种基于微分熵及卷积神经网络对运动想象四分类的识别方法。首先，将脑电信号通过滤波器提取为Alpha、Beta、Theta、Gamma 4个频段，分别计算各个频段的微分熵特征，并按照脑电极空间特征对数据结构进行重构为三维脑电信号特征立方体。最后，将其输入卷积神经网络进行四分类，该方法基于BCI Competition IV-2a公开数据集，准确率达到95.88%，并在试验室建立四分类运动想象数据集进行相同的处理，准确率达到94.50%。测试结果表明本文所提方法具有更好的识别效果。
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.

相似文献/References:

[1]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
　[J].Chinese Journal of Medical Physics,2017,34(3):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[2]刘岩,李幼军,陈萌. 基于固有模态分解和深度学习的抑郁症脑电信号分类分析[J].中国医学物理学杂志,2017,34(9):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
　[J].Chinese Journal of Medical Physics,2017,34(3):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
[3]张俊,朱金汉,庄永东,等. 基于卷积神经网络CT/CBCT影像质量自动分析[J].中国医学物理学杂志,2018,35(5):557.[doi:DOI:10.3969/j.issn.1005-202X.2018.05.012]
　ZHANG Jun,ZHU Jinhan,ZHUANG Yongdong,et al. Automatic analysis of CT/CBCT image quality based on convolutional neural network[J].Chinese Journal of Medical Physics,2018,35(3):557.[doi:DOI:10.3969/j.issn.1005-202X.2018.05.012]
[4]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
　DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(3):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[5]申代友,库洪安,皮红英,等. 基于深度相机的老年跌倒监护系统[J].中国医学物理学杂志,2019,36(2):223.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.019]
　SHEN Daiyou,KU Hongan,PI Hongying,et al. Depth camera-based fall detection system for the elderly[J].Chinese Journal of Medical Physics,2019,36(3):223.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.019]
[6]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
　GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(3):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[7]王自强,刘洪运,石金龙,等.基于卷积神经网络的心电图心博识别[J].中国医学物理学杂志,2019,36(8):938.[doi:DOI:10.3969/j.issn.1005-202X.2019.08.015]
　WANG Ziqiang,LIU Hongyun,SHI Jinlong,et al.ECG heartbeat recognition based on convolution neural network[J].Chinese Journal of Medical Physics,2019,36(3):938.[doi:DOI:10.3969/j.issn.1005-202X.2019.08.015]
[8]纪春阳,徐秀林,王燕. 深度神经网络技术在肿瘤细胞识别中的应用[J].中国医学物理学杂志,2019,36(9):1113.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.022]
　JI Chunyang,XU Xiulin,WANG Yan. Application of deep neural network in tumor cell recognition[J].Chinese Journal of Medical Physics,2019,36(3):1113.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.022]
[9]徐航,随力,张靖雯,等.卷积神经网络在医学图像分割中的研究进展[J].中国医学物理学杂志,2019,36(11):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
　XU Hang,SUI Li,ZHANG Jingwen,et al.Progress on convolutional neural network in medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(3):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[10]蒋家良,罗勇,何奕松,等.特征区域再聚焦提升全卷积神经网络勾画较小靶区准确度[J].中国医学物理学杂志,2020,37(1):75.[doi:DOI:10.3969/j.issn.1005-202X.2020.01.015]
　JIANG Jialiang,LUO Yong,HE Yisong,et al.Feature area refocusing for improving the accuracy of small target area segmentations by fully convolutional networks[J].Chinese Journal of Medical Physics,2020,37(3):75.[doi:DOI:10.3969/j.issn.1005-202X.2020.01.015]

备注/Memo

备注/Memo:
【收稿日期】2023-11-19 【基金项目】北京市自然科学基金（6214034） 【作者简介】廉小亲，博士，教授，研究生导师，研究方向：智能信息处理技术，E-mail: lianxq@263.net 【通信作者】高超，博士，副教授，研究生导师，研究方向：智能检测与数据挖掘，E-mail: gaochao9158@btbu.edu.cn
更新日期/Last Update: 2024-03-27