Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (33): 235-238.DOI: 10.3778/j.issn.1002-8331.2008.33.071

• 工程与应用 • Previous Articles     Next Articles

Classification of motor imagery EEG signals based on energy entropy

HU Jian-feng,MU Zhen-dong,XIAO Dan   

  1. Institute of Information Technology,Jiangxi Bluesky University,Nanchang 330098,China
  • Received:2007-12-11 Revised:2008-03-06 Online:2008-11-21 Published:2008-11-21
  • Contact: HU Jian-feng


胡剑锋,穆振东,肖 丹   

  1. 江西蓝天学院 信息技术研究所,南昌 330098
  • 通讯作者: 胡剑锋

Abstract: Feature extraction and classification of EEG is core issues on brain computer interface.The energy entropy of different motor imagery EEG signals is used to extract features.Finally,classification of Motor Imagery EEG is performed by a method based on the statistical theory.The results show that classification accuracy exceed 90%。

Key words: Brain Computer Interface(BCI), electroencephalogram(EEG), energy entropy

摘要: 对脑电信号进行特征提取和分类是脑机接口研究的核心问题,利用不同运动想象脑电信号能量熵的变化,从能量熵中提取特征,利用自定义基于统计理论分类方法进行分类,结果均达到90%以上。

关键词: 脑机接口, 脑电信号, 能量熵