期刊文献+

基于邻域空间模式的运动相关电位特征提取方法 被引量:1

An Approach to Extracting Features of Movement-Related Potentials Based on Neighborhood Spatial Pattern
下载PDF
导出
摘要 为解决脑-机接口(BCI)研究中所采集的脑电图(EEG)信号数据分布复杂和训练样本不足的问题,文中提出了一种新的特征提取方法——邻域空间模式(NSP)算法,用于提取BCI想象肢体运动分类算法中使用的重要分类特征——运动相关电位(MRPs).NSP算法不需要对样本的数据分布进行假设,主要利用样本的邻域关系和类别信息寻找最佳投影方向,使得映射后邻域内异类样本距离之和与同类样本距离之和的比值最大化.采用BCI竞赛2003和2001的其中两组数据进行实验,结果表明NSP算法能更有效地提取MRPs特征. In order to remedy the complex distribution of recorded electroencephalogram (EEG) data and the shortage of training data in terms of brain-computer interface ( BCI), a novel approach named neighborhood spatial pattern (NSP) is proposed to extract movement-related potentials (MRPs), which constitute the most important fea- tures utilized in the classification algorithms for the motor-imagery-based BCI. NSP searches the optimal direction which maximizes the ratio of the between-class distance to the within-class distance of the neighborhood in the pro- jected space. During the search, no assumptions about the latent data distribution should be made, and only the neighborhood relationship and the label information are required. NSP is also applied to two datasets from BCI com- petitions 2003 and 2001. The results show that NSP can effectively extract MPRs features.
出处 《华南理工大学学报(自然科学版)》 EI CAS CSCD 北大核心 2009年第10期11-15,共5页 Journal of South China University of Technology(Natural Science Edition)
基金 国家自然科学基金重点项目(U0635001) 国家自然科学基金资助项目(60505005)
关键词 邻域 特征提取 脑-机接口 运动相关电位 事件相关失同步/同步 neighborhood feature extraction brain-computer interface movement-related potential event-relateddesynchronization/synchronization
  • 相关文献

参考文献11

  • 1Wolpaw J R, Birbaumer N, McFarland D J, et al. Braincomputer interfaces for communication and control [ J ]. Clinical Neurophysiology ,2002,113 ( 6 ) :767-791.
  • 2Pfurtscheller G, Lopes da Silva FH. Event-related EEG/ MEG synchronization and desynchronization: basic principles [J]. Clinical Neurophysiology, 1999, 110 ( 11 ) : 1842-1857.
  • 3Pineda J A, Allison B Z, Vankov A. The effects of self- movement, observation, and imagination on mu rhythms and readiness potentials (RP's) :toward a brain-computer interface ( BCl ) [ J ]. IEEE Transactions on Rehabilitation Engineering, 2000,8 (2) : 219- 222.
  • 4Muller-Gerking J, Pfurtscheller G, Flyvbjerg H. Designing optimal spatial filters for single-trial EEG classification in a movement task [ J]. Clinical Neurophysiology, 1999, 110(5) :787-798.
  • 5Liao X, Yao D Z, Wu D. Combining spatial filters for the classification of single-trial EEG in a finger movement task [ J ]. IEEE Transactions on Biomedical Engineering, 2007,54(5) : 821-831.
  • 6Fu Y,Yan S C, Huang T S. Classification and feature extraction by simplexization [ J ]. IEEE Transactions on Information Forensics and Security ,2008,3 ( 1 ) :91-100.
  • 7Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding [ J ].Science, 2000, 290 : 2 323- 2 326.
  • 8Wang F, Zhang C S. Label propagation through linear neighborhoods [ J]. IEEE Transactions on Knowledge and Data Engineering,2008,20:55-67.
  • 9He X F, Niyogi P. Locality preserving projection [ C ] // Proceedings of the Conference on Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2003,16 : 153-160.
  • 10Fraunhofer-FIRST, Intelligent Data Analysis Group, Freie Universitat Berlin, et al. Data set < self-paced 1 s > [ EB/OL]. ( 2003- 05-15 ) [ 2008-10- 03 ]. http ://ida. first. fraunhofer. de/projects/bei/competition_ii/berlin_ desc. html.

同被引文献6

引证文献1

二级引证文献16

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部