Learning optimal spatial filters by discriminant analysis for brain-computer-interface
文献类型：期刊论文
| 作者 | Pang, Yanwei2; Yuan, Yuan1 ; Wang, Kongqiao3
|
| 刊名 | neurocomputing
 |
| 出版日期 | 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. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | computer science ; artificial intelligence |
| 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 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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