論文

査読有り
2011年

Improving Classification Performance of BCIs by Using Stationary Common Spatial Patterns and Unsupervised Bias Adaptation

HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, PART II
  • Wojciech Wojcikiewicz
  • ,
  • Carmen Vidaurre
  • ,
  • Motoaki Kawanabe

6679
開始ページ
34
終了ページ
41
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
10.1007/978-3-642-21222-2_5
出版者・発行元
SPRINGER-VERLAG BERLIN

Non-stationarities in EEG signals coming from electrode artefacts, muscular activity or changes of task involvement can negatively affect the classification accuracy of Brain-Computer Interface (BC!) systems. In this paper we investigate three methods to alleviate this: (1) Regularization of Common Spatial Patterns (CSP) towards stationary subspaces in order to reduce the influence of artefacts. (2) Unsupervised adaptation of the classifier bias with the goal to account for systematic shifts of the features occurring for example in the transition from calibration to feedback session or with increasing fatigue of the subject. (3) Decomposition of the CSP projection matrix into a whitening and a rotation part and adaptation of the whitening matrix in order to reduce the influence of non-task related changes. We study all three approaches on a data set of 80 subjects and show that stationary features with bias adaptation significantly outperforms the other combinations.

Web of Science ® 被引用回数 : 5

リンク情報
DOI
https://doi.org/10.1007/978-3-642-21222-2_5
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000297712800005&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/conf/hais/hais2011-2.html#conf/hais/WojcikiewiczVK11
ID情報
  • DOI : 10.1007/978-3-642-21222-2_5
  • ISSN : 0302-9743
  • DBLP ID : conf/hais/WojcikiewiczVK11
  • Web of Science ID : WOS:000297712800005

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