[1]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):1457-1461.[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(12):1457-1461.[doi:DOI:10.3969/j.issn.1005-202X.2018.12.017]
点击复制

 基于多变量格兰杰因果关系的运动想象因效网络构建()
分享到：

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

卷:
35卷
期数:
2018年第12期
页码:
1457-1461
栏目:
医学信号处理与医学仪器
出版日期:
2018-12-24

文章信息/Info

Title:
 Construction of motion imagination causal network based on multivariable Granger causality
文章编号:
1005-202X（2018）12-1457-05
作者:
 苌文清; 孙曜
 杭州电子科技大学自动化学院， 浙江 杭州 310018
Author(s):
 CHANG Wenqing;  SUN Yao
 School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
关键词:
多变量格兰杰因果关系; 运动想象; 因效网络
Keywords:
 ; Keywords: multivariable Granger causality;  motion imagination;  causal network
分类号:
R319；TP3
DOI:
DOI:10.3969/j.issn.1005-202X.2018.12.017
文献标志码:
A
摘要:
 运动想象神经活动规律的探索为脑损伤肢体瘫痪患者康复训练新方法研发等提供理论指导。基于格兰杰因果关系构建的因效网络是分析运动想象神经活动状态的重要工具，但是格兰杰因果关系只能反映两个变量之间的相互作用，而一个简单的运动想象过程也需要多个神经节点参与，针对该问题，本研究引入可反映一个集群中多个变量之间相互作用的多变量格兰杰因果分析，优化运动想象因效网络构建方法。针对4位受试者，利用多变量及传统格兰杰因果关系，分别构建同一人两种不同运动想象模式的因效网络，并提取网络特征进行运动想象模式分类。结果表明，基于多变量格兰杰因效网络进行4位受试者运动想象模式分类的正确率分别为90.4%、88.8%、91.1%、90.3%，基于格兰杰因效网络的正确率为88.5%、89.3%、90.2%、89.7%。与传统格兰杰因果关系相比，基于多变量格兰杰因果关系构建因效网络，能更准确地反映运动想象神经活动特征状态。
Abstract:
 Abstract: The exploration of the rules of neural activity in motion imagination can provide theoretical guidance for the development of new methods of rehabilitation training for patients with cerebral injuries and acroparalysis. The causal network constructed based on Granger causality is an important tool for analyzing the state of neural activity in motion imagination. However, Granger causality can only reflect the interaction between two variables, and a simple motion imagination process requires multiple neural nodes to participate. To solve this problem, a multivariable Granger causal analysis that can reflect the interaction among multiple variables in a cluster is introduced to optimize the construction method of motion imagination causal network. For the 4 subjects, multivariate and traditional Granger causality relationships are used to construct two different motion imaging patterns of the same subject, and the network characteristics are extracted to classify the motion imaginary patterns. The results show that the accuracy rate of motion imaging pattern classification in 4 subjects is 90.4%, 88.8%, 91.1%, and 90.3% in multivariable Granger causal network, as compared with 88.5%, 89.3%, 90.2%, 89.7% in traditional Granger causal network. Compared with the traditional Granger causality, causal network based on multivariable Granger causality can more accurately reflect the characteristics of neural activity in motion imagination.

相似文献/References:

[1]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(12):81.[doi:DOI:10.3969/j.issn.1005-202X.2019.01.016]
[2]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(12):590.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.019]
[3]吴拾瑶,随力,杨兰,等.运动想象重塑脑功能的研究进展[J].中国医学物理学杂志,2021,38(11):1449.[doi:DOI:10.3969/j.issn.1005-202X.2021.11.023]
　WU Shiyao,SUI Li,YANG Lan,et al.Research advances in motor imagery for remodeling brain functions[J].Chinese Journal of Medical Physics,2021,38(12):1449.[doi:DOI:10.3969/j.issn.1005-202X.2021.11.023]
[4]贾婷婷,董朝轶,马爽,等.基于互信息特征提取的运动想象脑机接口[J].中国医学物理学杂志,2022,39(1):63.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.011]
　JIA Tingting,DONG Chaoyi,et al.Brain-computer interface of motion imagery based on mutual information-based feature extraction[J].Chinese Journal of Medical Physics,2022,39(12):63.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.011]
[5]李红利,丁满,张荣华,等.基于特征融合神经网络的运动想象脑电分类算法[J].中国医学物理学杂志,2022,39(1):69.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.012]
　LI?ongli,ING Man,HANG?onghua,et al.Motor imagery EEG classification algorithm based on feature fusion neural network[J].Chinese Journal of Medical Physics,2022,39(12):69.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.012]
[6]戴亮宙,王娆芬,王海玲.基于特征融合AEBGNet的运动想象脑电分类算法[J].中国医学物理学杂志,2024,41(8):1021.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.016]
　DAI Liangzhou,WANG Raofen,WANG Hailing.Motor imagery EEG classification algorithm using feature fusion based AEBGNet[J].Chinese Journal of Medical Physics,2024,41(12):1021.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.016]

备注/Memo

备注/Memo:
 【收稿日期】2018-06-09
【基金项目】国家自然科学基金（61671197）
【作者简介】苌文清，硕士研究生，研究方向：模式识别，E-mail: wenqi-
ng1002@126.com
【通信作者】孙曜，博士，高级实验师，研究方向：生物医学信号处理、模式识别，E-mail: sunyao@hdu.edu.cn
更新日期/Last Update: 2018-12-26