Presentation 2014-11-17
Importance-Weighted Covariance Estimation for Robust Common Spatial Pattern
Alessandro BALZI, Florian YGER, Masashi SUGIYAMA,
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Abstract(in English) Non-stationarity is an important issue for practical applications of machine learning methods. This issue particularly affects Brain-Computer Interfaces (BCI) and tends to make their use difficult. In this paper, we show a practical way to make Common Spatial Pattern (CSP), a classical feature extraction that is particularly useful in BCI, robust to non-stationarity. To do so, we did not modify the CSP method itself, but rather make the covariance estimation (used as input by every CSP variant) more robust to non-stationarity. Those robust estimators are derived using a classical importance-weighting scenario. Finally, we highlight the behaviour of our robust framework on a toy dataset and show gains of accuracy on a real-life BCI dataset.
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Keyword(in English) Covariance estimation / Common Spatial Pattern / Brain-Computer Interface
Paper # IBISML2014-40
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Conference Information
Committee IBISML
Conference Date 2014/11/10(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Importance-Weighted Covariance Estimation for Robust Common Spatial Pattern
Sub Title (in English)
Keyword(1) Covariance estimation
Keyword(2) Common Spatial Pattern
Keyword(3) Brain-Computer Interface
1st Author's Name Alessandro BALZI
1st Author's Affiliation Department of Electronics, Information and Bioengineering, Politecnico di Milano()
2nd Author's Name Florian YGER
2nd Author's Affiliation Department of Complexity Science and Engineering, University of Tokyo
3rd Author's Name Masashi SUGIYAMA
3rd Author's Affiliation Department of Complexity Science and Engineering, University of Tokyo
Date 2014-11-17
Paper # IBISML2014-40
Volume (vol) vol.114
Number (no) 306
Page pp.pp.-
#Pages 8
Date of Issue