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.

Highlights

  • Evoked potentials (EPs) and ongoing brain activity oscillations, obtained by scalp electroencephalogram (EEG) recordings, have been linked with various cognitive processes and provide means for studying cerebral brain function [1]

  • We investigate the performance of the method when the signal subspace is enhanced by rejecting eye-related artifacts with the use of independent component analysis (ICA)

  • We presented a new dynamical estimation method for singletrial EP estimation based on a state-space representation for the trial-to-trial evolution of EP characteristics

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Summary

A Subspace Method for Dynamical Estimation of Evoked Potentials

It is a challenge in evoked potential (EP) analysis to incorporate prior physiological knowledge for estimation. Prior information about phase-locked properties of the EPs is assesed by means of estimated signal subspace and eigenvalue decomposition. 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 the effect of strong artifacts, 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

INTRODUCTION
Linear estimation and additive noise model
State-space modeling of EPs
Kalman filter and smoother algorithms
Signal and noise subspaces
Artifact correction by ICA
RESULTS
Measurements and artifact removal
Single-trial estimation
DISCUSSION AND CONCLUSION

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