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  1. B200 工学部/工学研究科
  2. B200a 雑誌掲載論文
  3. 学術雑誌

Application of support vector machines to reliability-based automatic repeat request for brain-computer interfaces

http://hdl.handle.net/2237/13909
http://hdl.handle.net/2237/13909
05f3bf8c-2661-46d2-9a81-c1a53178db41
名前 / ファイル ライセンス アクション
kokusai-0909.pdf kokusai-0909.pdf (294.7 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2010-08-02
タイトル
タイトル Application of support vector machines to reliability-based automatic repeat request for brain-computer interfaces
言語 en
著者 Takahashi, Hiromu

× Takahashi, Hiromu

WEKO 38159

en Takahashi, Hiromu

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Yoshikawa, Tomohiro

× Yoshikawa, Tomohiro

WEKO 38160

en Yoshikawa, Tomohiro

Search repository
Furuhashi, Takeshi

× Furuhashi, Takeshi

WEKO 38161

en Furuhashi, Takeshi

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
抄録
内容記述 A brain-computer interface (BCI) is a system that could enable patients like those with amyotrophic lateral sclerosis to control some equipment and to communicate with other people, and has been anticipated to be achieved. One of the problems in BCI research is a trade-off between speed and accuracy, and it is practically important to adjust those two performance measures effectively. So far the authors have considered BCIs as communications between users and computers, and have proposed an error control method, reliability-based automatic repeat request (RB-ARQ). It has been shown that, with linear discriminant analysis (LDA) as a classifier, RB-ARQ is more effective than other error control methods. In this paper, support vector machines (SVMs), one of the most popular classifiers, are applied to RB-ARQ. A quantitative comparison showed that there was no significant difference between LDA and SVM. Also, it was demonstrated that RB-ARQ improved the accuracy from the one acquired by the top ranked methods in the BCI competition to 100 percents, with less loss of the speed.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 IEEE
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1109/IEMBS.2009.5333543
ISSN
収録物識別子タイプ PISSN
収録物識別子 1557-170X
書誌情報 en : Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009)

p. 6457-6460, 発行日 2009-09-03
フォーマット
application/pdf
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://hdl.handle.net/2237/13909
識別子タイプ HDL
URI
識別子 http://dx.doi.org/10.1109/IEMBS.2009.5333543
識別子タイプ DOI
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