[1]逯鹏,牛新,刘素杰,等.运动准备电位单次检测技术研究[J].郑州大学学报(工学版),2018,39(04):70-74.[doi:10.13705/j.issn.1671-6833.2018.04.003]
　Lu Peng,Niu Xin,Liu Sujie,et al.Study on Single Trial Detection of Readiness Potentials[J].Journal of Zhengzhou University (Engineering Science),2018,39(04):70-74.[doi:10.13705/j.issn.1671-6833.2018.04.003]
点击复制

运动准备电位单次检测技术研究()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
39
期数:
2018年04期
页码:
70-74
栏目:
出版日期:
2018-07-22

文章信息/Info

Title:
Study on Single Trial Detection of Readiness Potentials
作者:
逯鹏; 牛新; 刘素杰; 胡玉霞; 胡航航
郑州大学 电气工程学院,河南 郑州,450001
Author(s):
Lu Peng; Niu Xin; Liu Sujie; Hu Yuxia; Hu Hanghang
School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan 450001
关键词:
Keywords:
EEG signal;  Myoelectric signal Motor;  readiness potential;  Wavelet packet transform;  Power spectrum analysis;  Support vector machine
DOI:
10.13705/j.issn.1671-6833.2018.04.003
文献标志码:
A
摘要:
针对自主运动起始时刻难以定位的难点，以受试者手臂自主运动前的EMG信号为研究对象，研究机电激活触发点作为自主运动起始时刻，然后确定有效时间段；针对运动准备电位频率段难以确定的问题，采用小波包变换与功率谱分析相融合的方法确定有效频段。以信号的能量、均值和方差为特征，采用支持向量机（SVM）进行RP单次检测。实验结果表明：在自主运动过程中单次检测RP中，15名受试者9组试验中最高检测率为77.5%-91.3%；每名受试者的9组平均检测率为68.2%-91.2%。研究结果有助于运动准备电位在异步BCI系统中的应用。
Abstract:
Aiming at locating the atarting time of autonomous motion, the EMG signal before the volunteers autonomous motion was taken as the research object. The EMG activation trigger point was selected as the starting time of autonomous motion, and then the effective time segment was determined. The frequency section of the motion preparation potential was difficult to be determined, the effective frequency band was determined by the method of combining the wavelet packet transform and the power spectrum analysis. The energy, mean and variance of the extracted signal were characterized by the support vector machine (SVM) for single detection of RP. The experimental resultts showed that: in the process of self motion of single detection RP, 15subjects in 9 experiment the highest detection rate was 77.5%-91.3%;each participant of the 9 groups the average detection rate was 68.2%-91.2%. The results of this paper could be useful to the application of motion preparation potential in asynchronous BCI system.

相似文献/References:

[1]彭金柱、董梦超、杨扬.基于视觉和肌电信息融合的手势识别方法[J].郑州大学学报(工学版),2021,42(02):68.[doi:10.13705/j.issn.1671-6833.2021.02.014]
　Peng Jinzhu,Dong Mengchao,Yang Yang,et al.Human Gesture Recognition Method Based on Vision and EMG Signal Information[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):68.[doi:10.13705/j.issn.1671-6833.2021.02.014]

更新日期/Last Update: 2018-07-26