Adaptive classification by hybrid EKF with truncated filtering: Brain computer interfacing

Ji Won Yoon, Stephen J. Roberts, Matthew Dyson, John Q. Gan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings
Pages370-377
Number of pages8
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008 - Daejeon, Korea, Republic of
Duration: 2008 Nov 22008 Nov 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5326 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008
CountryKorea, Republic of
CityDaejeon
Period08/11/208/11/5

Keywords

  • Extended Kalman filter
  • Informative observation
  • Logistic classification
  • Truncated filtering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yoon, J. W., Roberts, S. J., Dyson, M., & Gan, J. Q. (2008). Adaptive classification by hybrid EKF with truncated filtering: Brain computer interfacing. In Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings (pp. 370-377). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5326 LNCS). https://doi.org/10.1007/978-3-540-88906-9-47