[1]贾婷婷,董朝轶,马爽,等.基于互信息特征提取的运动想象脑机接口[J].中国医学物理学杂志,2022,39(1):63-68.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.011]
 JIA Tingting,DONG Chaoyi,et al.Brain-computer interface of motion imagery based on mutual information-based feature extraction[J].Chinese Journal of Medical Physics,2022,39(1):63-68.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.011]
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基于互信息特征提取的运动想象脑机接口()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第1期
页码:
63-68
栏目:
医学信号处理与医学仪器
出版日期:
2022-01-17

文章信息/Info

Title:
Brain-computer interface of motion imagery based on mutual information-based feature extraction
文章编号:
1005-202X(2022)01-0063-06
作者:
贾婷婷12董朝轶12马爽12马鹏飞12陈晓艳12肖志云12齐咏生12
1.内蒙古工业大学电力学院, 内蒙古 呼和浩特 010088; 2.内蒙古机电控制重点实验室, 内蒙古 呼和浩特 010051
Author(s):
JIA Tingting1 2 DONG Chaoyi1 2 MA Shuang1 2 MA Pengfei1 2 CHEN Xiaoyan1 2 XIAO Zhiyun1 2 QI Yongsheng1 2
1. College of Electric Power, Inner Mongolia University of Technology, Hohhot 010088, China 2. Inner Mongolia Key Laboratory of Electromechanical Control, Hohhot 010051, China
关键词:
互信息运动想象自回归模型支持向量机脑机接口
Keywords:
Keywords: mutual information motion imagery autoregressive model support vector machine brain-computer interface
分类号:
R318;TP14
DOI:
DOI:10.3969/j.issn.1005-202X.2022.01.011
文献标志码:
A
摘要:
脑机接口是一种实现计算机和人脑及其他设备间通信的系统。本文引入F3、F4、C3、C4、FZ、CZ、FC1、FC2、FC5、FC6等多通道运动想象脑电信号的网络连接结构权值等特征,采用支持向量机对不同的运动想象任务进行分类。对所提出的基于互信息(MI)的脑网络结构特征提取方法同传统方法自回归模型(AR)参数特征提取方法进行对比研究,发现基于MI特征提取的运动想象脑电信号分类正确率显著高于AR参数特征提取方法,将两类特征进行融合后,运动想象脑电信号分类正确率又显著高于单独使用MI或AR特征提取方法。
Abstract:
Abstract: Brain-computer interface is a kind of system that realizes communication between computers and human brains and other devices. The features such as the network connection structure weights of multi-channel motion imagery clectroencephalogram (EEG) signals from F3, F4, C3, C4, FZ, CZ, FC1, FC2, FC5, FC6, etc are introduced in the study, and support vector machine is used to classify different motion imagery tasks. The proposed brain network structure feature extraction based on mutual information (MI) is compared with the traditional autoregressive model-based parameter feature extraction. It is found that the accuracy rate of the MI-based feature extraction for motion imagery EEG signals classification is significantly higher than that of the autoregressive model-based parameter feature extraction. After the two types of features are fused, the classification accuracy rate of the constructed brain-computer interface classifier is significantly higher than that of feature extraction method using MI or autoregressive model alone.

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备注/Memo

备注/Memo:
【收稿日期】2021-07-18 【基金项目】国家自然科学基金(61364018, 61863029, 61763037, 61661042);内蒙古自然科学基金(2016JQ07, 2020MS06020);内蒙古科技成果转化项目(CGZH2018129);内蒙古自治区科技计划项目(关键技术攻关计划项目) 【作者简介】贾婷婷,硕士研究生,研究方向:模式识别和智能控制,E-mail: 1477001904@qq.com 【通信作者】董朝轶,博士,教授,主要研究方向:复杂生物网络建模、仿真与网络结构辨识等,E-mail: dongchaoyi@hotmail.com
更新日期/Last Update: 2022-01-17