A Coherent Electroencephalogram-Electromyogram Synchronization Method for Driving Human Lower Limb Movements

doi:10.7652/xjtuxb202202016

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A Coherent Electroencephalogram-Electromyogram Synchronization Method for Driving Human Lower Limb Movements

更新时间：2025-05-08

- A Coherent Electroencephalogram-Electromyogram Synchronization Method for Driving Human Lower Limb Movements
- Vol. 56, Issue 2, Pages: 149-158(2022)
- 作者机构：
  
  1. 西安交通大学陕西省智能机器人重点实验室,西安,710049
  2. 西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202202016
  CLC： TP242
- Online First：10 February 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
A Coherent Electroencephalogram-Electromyogram Synchronization Method for Driving Human Lower Limb Movements[J]. 2022, 56(2): 149-158.
DOI：

A Coherent Electroencephalogram-Electromyogram Synchronization Method for Driving Human Lower Limb Movements[J]. 2022, 56(2): 149-158. DOI： 10.7652/xjtuxb202202016.

摘要

针对驱动下肢运动中脑肌电信息不同步现象导致的脑肌电融合识别稳定性差等问题

提出一种结合多元自回归(mvAR)模型及最大化相干性法则的驱动下肢运动脑肌电相干性分析及信息同步化方法。首先根据驱动下肢运动中神经冲动传导机制及运动控制原理

进行驱动下肢运动脑肌电产生原理及信息不同步现象分析; 其次

引入多元自回归模型

以下肢稳态力输出状态下的多次实验多通道脑肌电作为模型输入

迭代得到基于高维模型拟合的脑肌电时频相干性结果; 再次

确定显著相干频率及时刻

并利用最大化相干性法则将脑肌电时延量化

实现脑肌电同步; 最后

搭建下肢稳态力输出脑肌电同步采集和下肢运动意图识别实验平台并进行方法验证。实验结果表明

在下肢稳态力输出过程中

脑肌电相干性在beta频段呈现显著相干

各受试者左右腿脑肌电时延分布于10～40 ms之间

其中左腿平均时延为(23.3±11.4)ms

右腿平均时延为(19.8±4.8)ms

使用抵消时延后的脑肌电融合识别下肢运动意图准确率有部分提升

可有效同步脑肌电中的驱动下肢运动信息

同时提升脑肌电融合识别稳定性。

Abstract

A novel method combining multivariate autoregressive(mvAR)model and maximizing coherence principle is proposed to analyze electroencephalogram(EEG)-electromyogram(EMG)coherence and enhance EEG-EMG information synchronization for driving human lower limb movements

and to solve the problem of performance degeneration of pattern recognition of lower limb movement based on EEG-EMG fusion. Firstly

according to the conduction mechanism of human nerve impulse and principle of movement control in lower limb movement

the generation principles of biological signals and phenomenon of information desynchronization of EEG and EMG are analyzed. Then a high dimension mvAR model is introduced with inputs of multi-channel

multi-trail EEG and EMG signals to obtain time-frequency coherence iteratively. Next the frequency and time when significant coherence occursi are determined based on time-frequency coherence analysis. Time delay between EEG and EMG is quantified by using the maximizing coherence principle to realize synchrony of EEG and EMG. Finally

experimental platforms for EEG and EMG acquisition under condition of steady state force output and lowen limb motion intention recognition are established to verify the method. Experimental results show that in the process of steady-state force output of lower limbs

significant coherence between EEG and EMG occurs in beta band

and time delay of each subject's left and right legs is distributed between 10-40 ms

in which the average time delay of left leg is(23.3±11.4)ms and the average time delay of right leg is(19.8±4.8)ms. The classification accuracy of pattern recognition of lower limb movement intention by using fusion of EEG and EMG with compensation of time delay is partially improved

which can effectively synchronize the information of driving lower libm movement in EEG and EMG and raise the classification stability of EGG fusion

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references

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