Dynamic Behavior Analysis of Electroencephalogram Information Transmission in Human Brain Default Mode Network

LI Jiajia; FENG Zhao; ZHAO Liang

doi:10.7652/xjtuxb202306003

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Dynamic Behavior Analysis of Electroencephalogram Information Transmission in Human Brain Default Mode Network

更新时间：2025-05-08

- Dynamic Behavior Analysis of Electroencephalogram Information Transmission in Human Brain Default Mode Network
- Vol. 57, Issue 6, Pages: 18-28(2023)
- 作者机构：
  
  西安建筑科技大学信息与控制工程学院,西安,710055
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202306003
  CLC： TN911.7
- Online First：10 June 2023，
  
  Published：2023
- 稿件说明：
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LI Jiajia, FENG Zhao, ZHAO Liang. Dynamic Behavior Analysis of Electroencephalogram Information Transmission in Human Brain Default Mode Network[J]. 2023, 57(6): 18-28.
DOI：

LI Jiajia, FENG Zhao, ZHAO Liang. Dynamic Behavior Analysis of Electroencephalogram Information Transmission in Human Brain Default Mode Network[J]. 2023, 57(6): 18-28. DOI： 10.7652/xjtuxb202306003.

摘要

为探究人体进行运动相关任务时大脑默认模式网络与其他脑区间的连接规律

基于运动及运动想象脑电信号公开数据集

通过快速傅里叶变换对不同状态下的脑电信号划分频段并进行滑动加窗处理

得到静息与运动态下同皮层不同频段的平均时间序列。采用动态传递熵的构造方法

分析默认模式网络到运动皮层、默认模式网络到前额叶皮层的信息交互

并在多个数据集下进行显著性检验。将动态传递熵的分析方法结合逻辑回归、决策树、XGBoost算法对不同任务进行分类研究

结果表明:大脑不同皮层功能区在进行不同任务时具有显著差异; 相较于静息态

前额叶皮层和运动皮层在运动过程中更加活跃

γ频段相较其他频段也更加活跃; 相较于静息态

运动态及运动想象态下默认模式网络到运动皮层、默认模式网络到前额叶皮层的信息交互都更强烈

其结果在不同样本空间的显著性检验P值小于0.025

此方法稳健; 脑电信号动态传递熵方法可以将机器学习算法的分类准确率提高20%以上。研究可为运动态脑电信号辨识提供理论参考。

Abstract

To explore the connection rule of the default mode network(DMN)of the brain and other brain areas when the human body performs motor-related tasks

based on the public dataset of motor and motor imagery electroencephalogram(EEG)signals

the EEG signals in different states were divided into frequency bands by fast Fourier transform(FFT)and subjected to sliding windowing processing

and the average time sequences of different frequency bands in the same cortex in resting and moving states were obtained. The construction method of dynamic transfer entropy was used to analyze the information interaction from the default mode network to the motor cortex and the default mode network to the prefrontal cortex

and the significance test was performed on multiple data sets. Finally

the dynamic transfer entropy analysis method was combined with the logic regression

decision tree

and XGBoost algorithm for classified study of different tasks. The results showed that different functional regions of cerebral cortex showed significant differences when performing different tasks. Compared with the resting state

the prefrontal cortex and motor cortex were more active during the exercise

and the γ band was more active than other bands. Compared with the resting state

the information interaction between the default mode network and the motor cortex

and between the default mode network and the prefrontal cortex was more intense in the motor and motor imagery states. The P value of the significance test in different sample spaces was less than 0.025

indicating the robustness of the method. The electroencephalogram dynamic transfer entropy method can improve the classification accuracy of the machine learning algorithm by more than 20%. This study has provided a reliable theoretical reference for dynamic EEG signal identification.

关键词

Keywords

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