A Brain Functional Network Based on Continuous Wavelet Transform and Symbolic Transfer Entropy

被引：0
|
作者：
Li M.-A. [1 ,2 ,3 ]
Zhang Y.-Y. [1 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing
[3] Engineering Research Center of Digital Community（Ministry of Education）, Beijing
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2022年 / 50卷 / 07期
关键词：
brain functional network; brain-computer interface; continuous wavelet transform; motor imagery electroencephalography; symbolic transfer entropy;
D O I：
10.12263/DZXB.20210298
中图分类号：
学科分类号：
摘要：
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. © 2022 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：1600 / 1608
页数：8
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