[1]吴叶兰,曹璞刚,徐梦,等.基于互信息与自适应图卷积的运动想象脑电信号识别[J].中国医学物理学杂志,2025,42(2):232-239.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.014]
　WU Yelan,CAO Pugang,XU Meng,et al.Motor imagery electroencephalogram signal recognition based on mutual information and adaptive graph convolution[J].Chinese Journal of Medical Physics,2025,42(2):232-239.[doi:DOI:10.3969/j.issn.1005-202X.2025.02.014]
点击复制

基于互信息与自适应图卷积的运动想象脑电信号识别()
分享到：

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

卷:
42
期数:
2025年第2期
页码:
232-239
栏目:
医学信号处理与医学仪器
出版日期:
2025-01-20

文章信息/Info

Title:
Motor imagery electroencephalogram signal recognition based on mutual information and adaptive graph convolution
文章编号:
1005-202X（2025）02-0232-08
作者:
吴叶兰; 曹璞刚; 徐梦; 张跃; 廉小亲; 于重重
北京工商大学计算机与人工智能学院， 北京 100048
Author(s):
WU Yelan;  CAO Pugang;  XU Meng;  ZHANG Yue;  LIAN Xiaoqin;  YU Chongchong
School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
关键词:
运动想象; 脑电信号; 自适应图卷积; 互信息神经估计; 特征提取
Keywords:
Keywords: motor imagery electroencephalogram signal adaptive graph convolution mutual information neural estimation feature extraction
分类号:
R318；TN911.7
DOI:
DOI:10.3969/j.issn.1005-202X.2025.02.014
文献标志码:
A
摘要:
针对运动想象脑电信号非线性特征提取困难、难以有效捕获脑电通道间功能连接关系的问题，提出一种基于互信息和自适应图卷积的运动想象脑电信号分类识别方法。首先，对原始运动想象脑电信号进行子频带划分，提取频域信息；然后，采用互信息神经估计方法构建邻接矩阵，获取脑电信号的非线性关系；最后，设计一种结合CBAM的自适应图卷积网络捕获各通道间的动态关联强度，实现空频特征提取。在BCI Competition IV 2a和BCI Competition III 3a数据集上，分别达到83.14%和88.19%的平均准确率，结果表明本文方法能有效获得脑电通道间功能连接关系，为运动想象脑电信号解码提供新思路。
Abstract:
To address the challenges of extracting nonlinear features from motor imagery electroencephalogram (EEG) signals and effectively capturing functional connectivity between EEG channels, a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network. The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals, uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method, and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module. On the BCI Competition IV 2a and BCI Competition III 3a datasets, the proposed method has average accuracies of 83.14% and 88.19%, respectively, demonstrating that it can effectively reveal functional connectivity between EEG channels, providing a new approach for decoding motor imagery EEG signals.

相似文献/References:

[1]王怡玲,覃玉荣,郭湛超,等.基于不同闪烁频率光刺激的脑电压变化研究[J].中国医学物理学杂志,2014,31(05):5184.[doi:10.3969/j.issn.1005-202X.2014.05.019]
[2]杨建平,张德乾,吕敬祥,等.操作发起过程多脑区协作的脑电谱熵特征[J].中国医学物理学杂志,2016,33(1):44.[doi:DOI:10.3969/j.issn.1005-202X.2016.01.010]
　[J].Chinese Journal of Medical Physics,2016,33(2):44.[doi:DOI:10.3969/j.issn.1005-202X.2016.01.010]
[3]崔招焕,等.大鼠癫痫脑电信号采集[J].中国医学物理学杂志,2016,33(2):118.[doi:10.3969/j.issn.1005-202X.2016.02.003]
　[J].Chinese Journal of Medical Physics,2016,33(2):118.[doi:10.3969/j.issn.1005-202X.2016.02.003]
[4]顾家军,叶继伦.麻醉深度监测中脑电信号特征提取方法[J].中国医学物理学杂志,2016,33(2):157.[doi:10.3969/j.issn.1005-202X.2016.02.010]
　[J].Chinese Journal of Medical Physics,2016,33(2):157.[doi:10.3969/j.issn.1005-202X.2016.02.010]
[5]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(2):963.[doi:DOI:10.3969/j.issn.1005-202X.2017.09.021]
[6]马玉良,刘卫星,张淞杰,等.基于ABC-SVM的运动想象脑电信号模式分类[J].中国医学物理学杂志,2018,35(9):1056.[doi:10.3969/j.issn.1005-202X.2018.09.012]
　MAYuliang,LIUWeixing,ZHANG Songjie,et al.Pattern classification of motor imagery EEG signals based on ABC-SVM algorithm[J].Chinese Journal of Medical Physics,2018,35(2):1056.[doi:10.3969/j.issn.1005-202X.2018.09.012]
[7]刘畅,覃玉荣,时文健.视听觉刺激下大脑头皮电位空间变化特性[J].中国医学物理学杂志,2018,35(10):1225.[doi:DOI:10.3969/j.issn.1005-202X.2018.010.023]
　LIU Chang,QIN Yurong,SHI Wenjian. Spatial variation characteristics of scalp potentials under audiovisual stimuli[J].Chinese Journal of Medical Physics,2018,35(2):1225.[doi:DOI:10.3969/j.issn.1005-202X.2018.010.023]
[8]苌文清,孙曜. 基于多变量格兰杰因果关系的运动想象因效网络构建[J].中国医学物理学杂志,2018,35(12):1457.[doi:DOI:10.3969/j.issn.1005-202X.2018.12.017]
　CHANG Wenqing,SUN Yao. Construction of motion imagination causal network based on multivariable Granger causality[J].Chinese Journal of Medical Physics,2018,35(2):1457.[doi:DOI:10.3969/j.issn.1005-202X.2018.12.017]
[9]姜月,邹任玲. 基于多特征融合的运动想象脑电信号识别研究[J].中国医学物理学杂志,2019,36(5):590.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.019]
　JIANG Yue,ZOU Renling. Recognition of motor imagery EEG signals based on multi-feature fusion[J].Chinese Journal of Medical Physics,2019,36(2):590.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.019]
[10]李冬,金韬,冯智英,等.基于脑电信号的疼痛强度识别方法研究[J].中国医学物理学杂志,2019,36(7):836.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.017]
　LI Dong,JIN Tao,FENG Zhiying,et al.Pain intensity recognition based on EEG signals[J].Chinese Journal of Medical Physics,2019,36(2):836.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.017]
[11]周杰,杨国雨,徐涛. 基于空间频率与时间序列信息的多类运动想象脑电分类[J].中国医学物理学杂志,2019,36(1):81.[doi:DOI:10.3969/j.issn.1005-202X.2019.01.016]
　ZHOU Jie,YANG Guoyu,XU Tao. Classification of multi-class motor imagery EEG data based on spatial frequency and time-series information[J].Chinese Journal of Medical Physics,2019,36(2):81.[doi:DOI:10.3969/j.issn.1005-202X.2019.01.016]

备注/Memo

备注/Memo:
【收稿日期】2024-10-26 【基金项目】跨行业协同应用与数据要素服务平台项目（CEIEC-2023-ZM02-0090） 【作者简介】吴叶兰，副教授，研究方向：智能信息处理、机器人技术，E-mail: wuyel@th.btbu.edu.cn
更新日期/Last Update: 2025-01-22