Sequential Bayesian estimation for adaptive classification

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

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

8 Citations (Scopus)

Abstract

This paper proposes a robust algorithm to adapt a model for EEG signal classification using a modified Extended Kalman Filter (EKF). By applying Bayesian conjugate priors and marginalising the parameters, we can avoid the needs to estimate the covariances of the observation and hidden state noises. In addition, Laplace approximation is employed in our model to approximate non-Gaussian distributions as Gaussians.

Original languageEnglish
Title of host publicationIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Pages601-605
Number of pages5
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI - Seoul, Korea, Republic of
Duration: 2008 Aug 202008 Aug 22

Other

Other2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI
CountryKorea, Republic of
CitySeoul
Period08/8/2008/8/22

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Keywords

  • Extended Kalman filter
  • Laplace approximation
  • Marginalisation
  • Nonlinear dynamics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

Cite this

Yoon, J. W., Roberts, S. J., Dyson, M., & Gan, J. Q. (2008). Sequential Bayesian estimation for adaptive classification. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (pp. 601-605). [4648010] https://doi.org/10.1109/MFI.2008.4648010