鲁先举,
周莹
杭州电子科技大学智能控制与机器人研究所 杭州 310018
基金项目:国家自然科学基金(61671197)
详细信息
作者简介:罗志增：男，1965年生，博士，教授，研究方向为生物信息处理与分析、机器人技术、传感器及多信息融合等
鲁先举：男，1995年生，硕士生，研究方向为模式识别、脑-机接口及相关应用等
周莹：男，1993年生，硕士生，研究方向为脑-机接口
通讯作者:罗志增 luo@hdu.edu.cn
中图分类号:TN911.7; TP391计量
文章访问数:623
HTML全文浏览量:294
PDF下载量:116
被引次数:0
出版历程
收稿日期:2019-12-19
修回日期:2020-12-07
网络出版日期:2020-12-16
刊出日期:2021-02-23
EEG Feature Extraction Based on Brain Function Network and Sample Entropy
Zhizeng LUO,,Xianju LU,
Ying ZHOU
Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018, China
Funds:The National Natural Science Foundation of China (61671197)
摘要
摘要:针对脑-机接口(BCI)研究中采用单一特征对运动想象脑电信号(EEG)识别率不高的问题，该文提出一种结合脑功能网络和样本熵的特征提取方法。根据事件相关同步/去同步(ERS/ERD)现象以及皮层与肢体运动想象间的对侧映射机制，选取小波包变换消噪重构后的
节律的样本熵构筑分布性和指向性相结合的特征向量。选用支持向量机(SVM)对左右手运动想象脑电信号进行分类，结果表明基于脑功能网络和样本熵的特征提取方法能够实现更优的分类效果，分类准确率最高可达90.27%。
关键词:脑电信号/
脑功能网络/
样本熵/
特征提取/
事件相关同步/去同步
Abstract:For the low recognition rate of motor imagery ElectroEncephaloGram (EEG) signals using single feature in Brain-Computer Interface (BCI) research, a feature extraction method combining brain function network and sample entropy is proposed. According to the neural mechanism appearing in Event Related Synchronization/Event Related Desynchronization (ERS/ERD) phenomenon and the contralateral mapping mechanism between cortex and limb motor imagery, the μ rhythm is denoised by wavelet packet transform. The brain function network is constructed for left hemispherical brain region and right hemispherical brain region by μ rhythm of 27 left channels and 27 right channels respectively. The mean node degree and the mean clustering coefficient are calculated as the brain function network characteristics, and the feature vectors combining the distribution and directivity are constructed by the sample entropy of C3 and C4 channels with the μ rhythm. The Support Vector Machine (SVM) is used to classify the left hand and right hand motor imagery EEG signals. The results show that the feature extraction method based on brain function network and sample entropy achieves better classification result, and the highest classification rate reached 90.27%.
Key words:ElectroEncephaloGram (EEG)/
Brain function network/
Sample entropy/
Feature extraction/
Event Related Synchronization/Event Related Desynchronization (ERS/ERD)
PDF全文下载地址:
https://jeit.ac.cn/article/exportPdf?id=a114ff22-6720-4a9e-a8f1-e39545efa103
