Title :
Sequential Bayesian estimation for adaptive classification
Author :
Yoon, Ji ; Roberts, Stephen ; Dyson, Matt ; Gan, John
Author_Institution :
Dept. of Eng. Sci., Univ. of Oxford, Oxford
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.
Keywords :
Bayes methods; Gaussian distribution; Kalman filters; approximation theory; electroencephalography; medical signal processing; signal classification; EEG signal classification; Gaussian distribution; Laplace approximation; adaptive classification; extended Kalman filter; sequential Bayesian estimation; Bayesian methods; Brain modeling; Computer interfaces; Electroencephalography; Gallium nitride; Gaussian approximation; Logistics; Noise robustness; Signal processing algorithms; State-space methods; Extended Kalman filter; Laplace Approximation; Marginalisation; Nonlinear dynamics;
Conference_Titel :
Multisensor Fusion and Integration for Intelligent Systems, 2008. MFI 2008. IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-2143-5
Electronic_ISBN :
978-1-4244-2144-2
DOI :
10.1109/MFI.2008.4648010
