Learning optimal spatial filters by discriminant analysis for brain-computer-interface

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Learning optimal spatial filters by discriminant analysis for brain-computer-interface
	Pang, Yanwei 2; Yuan, Yuan1 ; Wang, Kongqiao 3
刊名	neurocomputing
	2012-02-01
卷号	77 期号:1 页码:20-27
关键词	Neural interfaces Brain-computer interfaces Common spatial pattern EEG Discriminant analysis
ISSN号	0925-2312
产权排序	2
合作状况	国内
中文摘要	common spatial pattern (csp) is one of the most widespread methods for brain-computer interfaces (bci), which is capable of enhancing the separability of the brain signals such as multi-channel electroencephalogram (eeg). csp attempts to strengthen the separability by maximizing the variance of the spatially filtered signal of one class while minimizing it for another class. a straightforward way to improve the csp is to employ the fisher-rao linear discriminant analysis (flda). but for the two-class scenario in bci, flda merely result in as small as one filter. experimental results have shown that the number of spatial filter is too small to achieve satisfying classification accuracy. therefore, more than one filter is expected to get better performance. to deal with this difficulty, in this paper we propose to divide each class into many sub-classes (clusters) and formulate the problem in a re-designed graph embedding framework where the vertexes are cluster centers. we also reformulate the traditional flda in our graph embedding framework, which helps developing and understanding the proposed method. experimental results demonstrate the advantages of the proposed method.
英文摘要	common spatial pattern (csp) is one of the most widespread methods for brain-computer interfaces (bci), which is capable of enhancing the separability of the brain signals such as multi-channel electroencephalogram (eeg). csp attempts to strengthen the separability by maximizing the variance of the spatially filtered signal of one class while minimizing it for another class. a straightforward way to improve the csp is to employ the fisher-rao linear discriminant analysis (flda). but for the two-class scenario in bci, flda merely result in as small as one filter. experimental results have shown that the number of spatial filter is too small to achieve satisfying classification accuracy. therefore, more than one filter is expected to get better performance. to deal with this difficulty, in this paper we propose to divide each class into many sub-classes (clusters) and formulate the problem in a re-designed graph embedding framework where the vertexes are cluster centers. we also reformulate the traditional flda in our graph embedding framework, which helps developing and understanding the proposed method. experimental results demonstrate the advantages of the proposed method. (c) 2011 elsevier b.v. all rights reserved.
学科主题	computer science ; artificial intelligence
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence
研究领域[WOS]	computer science
关键词[WOS]	nonlinear dimensionality reduction ; model ; framework
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000298206400003
公开日期	2012-09-03
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/20263]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 2.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China 3.Nokia Res Ctr, Beijing 100176, Peoples R China
推荐引用方式 GB/T 7714	Pang, Yanwei,Yuan, Yuan,Wang, Kongqiao. Learning optimal spatial filters by discriminant analysis for brain-computer-interface[J]. neurocomputing,2012,77(1):20-27.
APA	Pang, Yanwei,Yuan, Yuan,&Wang, Kongqiao.(2012).Learning optimal spatial filters by discriminant analysis for brain-computer-interface.neurocomputing,77(1),20-27.
MLA	Pang, Yanwei,et al."Learning optimal spatial filters by discriminant analysis for brain-computer-interface".neurocomputing 77.1(2012):20-27.