論文

査読有り
2011年

Modelling Non-stationarities in EEG Data with Robust Principal Component Analysis

HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, PART II
  • Javier Pascual
  • ,
  • Motoaki Kawanabe
  • ,
  • Carmen Vidaurre

6679
開始ページ
51
終了ページ
58
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_7
出版者・発行元
SPRINGER-VERLAG BERLIN

Modelling non-stationarities is an ubiquitous problem in neuroscience. Robust models help understand the underlying cause of the change observed in neuroscientific signals to bring new insights of brain functioning. A common neuroscientific signal to study the behaviour of the brain is electro-encephalography (EEG) because it is little intrusive, relatively cheap and easy to acquire. However, this signal is known to be highly non-stationary. In this paper we propose a robust method to visualize non-stationarities present in neuroscientific data. This method is unaffected by noise sources that are uninteresting to the cause of change, and therefore helps to better understand the neurological sources responsible for the observed non-stationarity. This technique exploits a robust version of the principal component analysis and we apply it as illustration to EEG data acquired using a brain-computer interface, which allows users to control an application through their brain activity. Non-stationarities in EEG cause a drop of performance during the operation of the brain-computer interface. Here we demonstrate how the proposed method can help to understand and design methods to deal with non-stationarities.

Web of Science ® 被引用回数 : 2

リンク情報
DOI
https://doi.org/10.1007/978-3-642-21222-2_7
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000297712800007&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/conf/hais/hais2011-2.html#conf/hais/PascualKV11
ID情報
  • DOI : 10.1007/978-3-642-21222-2_7
  • ISSN : 0302-9743
  • DBLP ID : conf/hais/PascualKV11
  • Web of Science ID : WOS:000297712800007

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