Sequential Bayesian Estimation for Adaptive Classification
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....
| Hauptverfasser: | , , , , |
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| Format: | Conference item |
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2008
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