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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 61916, 11 pages
http://dx.doi.org/10.1155/2007/61916
Research Article

A Subspace Method for Dynamical Estimation of Evoked Potentials

Department of Physics, University of Kuopio, P.O.Box 1627, Kuopio 70211, Finland

Received 16 February 2007; Revised 7 June 2007; Accepted 18 September 2007

Academic Editor: Saied Sanei

Copyright © 2007 Stefanos D. Georgiadis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.