Matching Pursuit based Removal of Cardiac Pulse-Related Artifacts in EEG/fMRI

Cardiac pulse-related artifacts in the EEG recorded simultaneously with fMRI are complex and highly variable. Their effective removal is an unsolved problem. Our aim is to develop an adaptive removal algorithm based on the matching pursuit (MP) technique and to compare it to established methods using a visual evoked potential (VEP). We recorded the VEP inside the static magnetic field of an MR scanner (with artifacts) as well as in an electrically shielded room (artifact free). The MP-based artifact removal outperformed average artifact subtraction (AAS) and optimal basis set removal (OBS) in terms of restoring the EEG field map topography of the VEP. Subsequently, a dipole model was fitted to the VEP under each condition using a realistic boundary element head model. The source location of the VEP recorded inside the MR scanner was closest to that of the artifact free VEP after cleaning with the MP-based algorithm as well as with AAS. While none of the tested algorithms offered complete removal, MP showed promising results due to its ability to adapt to variations of latency, frequency and amplitude of individual artifact occurrences while still utilizing a common template.

Authors:

• Rainer Schneider
• Stephan Lau
• Levin Kuhlmann
• Simon Vogrin
• Maciej Gratkowski
• Mark Cook
• Jens Haueisen

Keywords:

• matching pursuit
• ballistocardiogram
• artifactremoval
• EEG/fMRI.

References:

[1] S. Debener, C. Kranczioch, and I. Gutberlet, "EEG Quality: Origin and
Reduction of the EEG Cardiac-Related Artefact," in EEG-fMRI:
Physiological Basis, Technique and Applications, C. Mulert and L.
Lemieux, Ed. Berlin: Springer, 2010, p. 539.
[2] P. J. Allen, G. Polizzi, K. Krakow, D. R. Fish, et al., "Identification of
EEG events in the MR scanner: the problem of pulse artifact and a
method for its subtraction," NeuroImage, vol. 8, pp. 229-39, 1998.
[3] R. K. Niazy, C. F. Beckmann, G. D. Iannetti, J. M. Brady, and S. M.
Smith, "Removal of FMRI environment artifacts from EEG data using
optimal basis sets," NeuroImage, vol. 28, pp. 720-37, 2005.
[4] A. Delorme and S. Makeig, "EEGLAB: an open source toolbox for
analysis of single-trial EEG dynamics including independent component
analysis," J Neurosci Methods, vol. 134, pp. 9-21, 2004.
[5] M. Gratkowski, J. Haueisen, L. Arendt-Nielsen, A. C. Chen, and F.
Zanow, "Decomposition of biomedical signals in spatial and timefrequency
modes," Methods Inf Med, vol 47, pp. 26-37, 2008.
[6] M. Gratkowski, J. Haueisen, L. Arendt-Nielsen, A.C. Chen, and F.
Zanow," Time-frequency filtering of MEG signals with matching
pursuit". J Physiol Paris, vol. 99, pp. 47-57, 2006.
[7] H. Schimmel, "The (+) reference: accuracy of estimated mean
components in average response studies," Science, vol. 157, pp. 92-4,
1967.
[8] J. W. Meijs, O. W. Weier, M. J. Peters, and A. van Oosterom, "On the
numerical accuracy of the boundary element method," IEEE Trans
Biomed Eng, vol 36, pp. 1038-49, 1989.