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It is a challenge in evoked potential (EP) analysis to incorporate prior physiological
knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP
characteristics estimation. Prior information about phase-locked properties of the EPs is assesed by
means of estimated signal subspace and eigenvalue decomposition. Then for those situations that
dynamic fluctuations from stimulus-to-stimulus could be expected, prior information can be exploited
by means of state-space modeling and recursive Bayesian mean square estimation methods (Kalman
filtering and smoothing). We demonstrate that a few dominant eigenvectors of the data correlation matrix
are able to model trend-like changes of some component of the EPs, and that Kalman smoother
algorithm is to be preferred in terms of better tracking capabilities and mean square error reduction. We
also demonstrate the effect of strong artifacts, particularly eye blinks, on the quality of the signal
subspace and EP estimates by means of independent component analysis applied as a prepossessing
step on the multichannel measurements
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