Modified gain extended Kalman filtering for estimation of visual evoked potentials

The present authors use an approach in which two different, parametrically-described models are considered for the spontaneous and the evoked parts of the measured activity. The model parameters are identified using a special formulation which converts the identification problem into a (nonlinear) filtering problem. Extended Kalman Filtering (EKF) technique is thus used for the identification of model parameters. Once the model parameters are obtained, Kalman filtering is used once more to obtain an estimate of the evoked part of the signal. Some modifications to the EKF algorithm have been incorporated in order to overcome divergence problems associated with the Extended Kalman Filter.<
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