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Adaptive Classification by Hybrid EKF with Truncated Filtering: Brain Computer Interfacing

  • Ji Won Yoon
  • Stephen J. Roberts
  • Matthew Dyson
  • John Q. Gan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

This paper proposes a robust algorithm for adaptive modelling of EEG signal classification using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis functions (RBF) and Autoregressive (AR) modeling and obtains better classification performance by truncating the filtering distribution when new observations are very informative.

Keywords

Extended Kalman Filter Informative Observation Logistic Classification Truncated Filtering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ji Won Yoon
    • 1
  • Stephen J. Roberts
    • 1
  • Matthew Dyson
    • 2
  • John Q. Gan
    • 2
  1. 1.Pattern Analysis and Machine Learning Group Department of Engineering ScienceUniversity of OxfordUK
  2. 2.Department of Computing and Electronic SystemsUniversity of EssexUK

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