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Sequential Bayesian Estimation for Adaptive Classification

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. ©2008 IEEE.
Publication status:
Published

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Publisher copy:
10.1109/MFI.2008.4648010

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Pages:
33-37
Publication date:
2008-01-01
DOI:
URN:
uuid:0620114d-e352-44b7-bd7e-e77d7bca7a6d
Source identifiers:
63747
Local pid:
pubs:63747
ISBN:
978-1-4244-2143-5

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