基于连续小波变换和符号传递熵的脑功能网络构建方法
A Brain Functional Network Based on Continuous Wavelet Transform and Symbolic Transfer Entropy
- 电子学报 2022年50卷第7期 页码：1600-1608
- 作者机构：
1.北京工业大学信息学部,北京 100124
2.计算智能与智能系统北京市重点实验室,北京 100124
3.教育部数字社区工程研究中心,北京 100124
- 作者简介：
[ "李明爱 女，1966年7月出生，河南鹤壁人.2006年于北京工业大学获得博士学位，现为北京工业大学教授、博士生导师，主要研究方向为脑机接口技术、人工智能与智能康复.E-mail: limingai@bjut.edu.cn" ]
[ "张圆圆 男，1996 年12 月出生，安徽安庆人. 现为北京工业大学信息学部硕士研究生，主要研究方向为脑机接口技术、信号处理与模式识别.E-mail: zyybjutemail@emails.bjut.edu.cn" ]
- 基金信息：国家自然科学基金(62173010;11832003)
DOI：10.12263/DZXB.20210298
中图分类号： TP18收稿：2021-03-02，
修回：2022-01-24，
纸质出版：2022-07-25
- 稿件说明：
移动端阅览
李明爱,张圆圆.基于连续小波变换和符号传递熵的脑功能网络构建方法[J].电子学报,2022,50(07):1600-1608.
LI Ming-ai,ZHANG Yuan-yuan.A Brain Functional Network Based on Continuous Wavelet Transform and Symbolic Transfer Entropy[J].ACTA ELECTRONICA SINICA,2022,50(07):1600-1608.
李明爱,张圆圆.基于连续小波变换和符号传递熵的脑功能网络构建方法[J].电子学报,2022,50(07):1600-1608. DOI： 10.12263/DZXB.20210298.
LI Ming-ai,ZHANG Yuan-yuan.A Brain Functional Network Based on Continuous Wavelet Transform and Symbolic Transfer Entropy[J].ACTA ELECTRONICA SINICA,2022,50(07):1600-1608. DOI： 10.12263/DZXB.20210298.
摘要
为有效利用运动想象脑电信号(Motor Imagery Electroencephalogram，MI-EEG)的频域信息并精确反映脑电极之间的非线性因果交互作用，本文提出一种基于连续小波变换和符号传递熵的脑功能网络构建方法.首先，对每导MI-EEG进行连续小波变换，求得其时-频-能量矩阵；然后，将与运动想象密切相关的频带内各频率所对应的时间-能量序列依次拼接，得到各导联的一维时频能量序列；最后，基于任意两电极时频能量序列间的符号传递熵计算连接矩阵，构建脑功能网络.实验结果表明，以电极时频能量序列间的符号传递熵构建的脑功能网络，能够有效反映MI-EEG的时频特征和非线性特征信息传递，相比于传统脑网络构建方法，更有利于增强不同运动想象任务的可分性.
Abstract
In order to utilize the frequency domain information of motor imagery electroencephalogram(MI-EEG) signals to effectively and accurately reflect the nonlinear causal interaction between different EEG electrodes
this paper presents a brain functional network based on continuous wavelet transform and symbolic transfer entropy. Firstly
the continuous wavelet transform is applied to each MI-EEG signal to compute the time-frequency-energy matrix. Then
the one-dimensional time-frequency energy sequence of each channel is obtained by joining serially spliced time-energy sequence in the frequency band closely related to motor imagery. Finally
the brain connectivity matrix is calculated based on the symbolic transfer entropy between the time-frequency energy sequences of any two channels
and the brain functional network is constructed.The experiment results show that the brain functional network constructed with the symbolic transfer entropy between time-frequency energy sequences can effectively reflect the time-frequency characteristics and nonlinear characteristic information transmission of MI-EEG. Compared with the traditional brain network construction method
it is beneficial to enhance the separability of different motor imagery tasks.
关键词
Keywords
references
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