A Subspace Method for Dynamical Estimation of  Evoked Potentials

Georgiadis, Stefanos D.; Ranta-aho, Perttu O.; Tarvainen, Mika P.; Karjalainen, Pasi A.

doi:https://doi.org/10.1155/2007/61916

Computational Intelligence and Neuroscience

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EEG/MEG Signal Processing

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Research Article | Open Access

Volume 2007 | Article ID 061916 | https://doi.org/10.1155/2007/61916

A Subspace Method for Dynamical Estimation of Evoked Potentials

Stefanos D. Georgiadis,¹Perttu O. Ranta-aho,¹Mika P. Tarvainen,¹and Pasi A. Karjalainen¹

Academic Editor: Saied Sanei

Received16 Feb 2007

Revised07 Jun 2007

Accepted18 Sept 2007

Published11 Nov 2007

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.

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Copyright

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.

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