2015年
Analyzing brain waves for activity recognition of learners
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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- 巻
- 9357
- 号
- 開始ページ
- 64
- 終了ページ
- 73
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1007/978-3-319-24315-3_7
- 出版者・発行元
- SPRINGER-VERLAG BERLIN
© IFIP International Federation for Information Processing 2015. Understanding the states of learners at a lecture is expected to be useful for improving the quality of the lecture. This paper is trying to recognize the activities of learners by their brain wave data for estimating the states. In analyses on brain wave data, generally, some particular bands such as α and β are considered as the features. The authors considered other bands of higher and lower frequencies to compensate for the coarseness of simple electroencephalographs. They conducted an experiment of recognizing two activities of five subjects with the brain wave data captured by a simple electroencephalograph. They applied support vector machine to 8-dimensional vectors which correspond to eight bands on the brain wave data. The results show that considering multiple bands yielded high accuracy compared with the usual features.
- リンク情報
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- DOI
- https://doi.org/10.1007/978-3-319-24315-3_7
- DBLP
- https://dblp.uni-trier.de/rec/conf/ict-eurasia/AbeKBTM15
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000366189100007&DestApp=WOS_CPL
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84951150936&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84951150936&origin=inward
- Dblp Cross Ref
- https://dblp.uni-trier.de/conf/ict-eurasia/2015
- Dblp Url
- https://dblp.uni-trier.de/db/conf/ict-eurasia/ict-eurasia2015.html#AbeKBTM15
- ID情報
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- DOI : 10.1007/978-3-319-24315-3_7
- ISSN : 0302-9743
- eISSN : 1611-3349
- DBLP ID : conf/ict-eurasia/AbeKBTM15
- ORCIDのPut Code : 33943842
- SCOPUS ID : 84951150936
- Web of Science ID : WOS:000366189100007