Robust common spatial filters with a maxmin approach

Motoaki Kawanabe, Wojciech Samek, Klaus Muller, Carmen Vidaurre

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Electroencephalographic signals are known to be nonstationary and easily affected by artifacts; therefore, their analysis requires methods that can deal with noise. In this work, we present a way to robustify the popular common spatial patterns (CSP) algorithm under a maxmin approach. In contrast to standard CSP that maximizes the variance ratio between two conditions based on a single estimate of the class covariance matrices, we propose to robustly compute spatial filters by maximizing the minimum variance ratio within a prefixed set of covariance matrices called the tolerance set. We show that this kind of maxmin optimization makes CSP robust to outliers and reduces its tendency to overfit. We also present a data-driven approach to construct a tolerance set that captures the variability of the covariance matrices over time and shows its ability to reduce the nonstationarity of the extracted features and significantly improve classification accuracy. We test the spatial filters derived with this approach and compare them to standard CSP and a state-of-the-art method on a real-world brain-computer interface (BCI) data set in which we expect substantial fluctuations caused by environmental differences. Finally we investigate the advantages and limitations of the maxmin approach with simulations.

Original languageEnglish
Pages (from-to)349-376
Number of pages28
JournalNeural Computation
Volume26
Issue number2
DOIs
Publication statusPublished - 2014 Jan 30

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Arts and Humanities (miscellaneous)

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    Kawanabe, M., Samek, W., Muller, K., & Vidaurre, C. (2014). Robust common spatial filters with a maxmin approach. Neural Computation, 26(2), 349-376. https://doi.org/10.1162/NECO_a_00544