{"title":"Matching Pursuit based Removal of Cardiac Pulse-Related Artifacts in EEG\/fMRI","authors":"Rainer Schneider, Stephan Lau, Levin Kuhlmann, Simon Vogrin, Maciej Gratkowski, Mark Cook,Jens Haueisen","volume":56,"journal":"International Journal of Biomedical and Biological Engineering","pagesStart":335,"pagesEnd":341,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/13568","abstract":"Cardiac pulse-related artifacts in the EEG recorded\r\nsimultaneously with fMRI are complex and highly variable. Their\r\neffective removal is an unsolved problem. Our aim is to develop an\r\nadaptive removal algorithm based on the matching pursuit (MP)\r\ntechnique and to compare it to established methods using a visual\r\nevoked potential (VEP). We recorded the VEP inside the static\r\nmagnetic field of an MR scanner (with artifacts) as well as in an\r\nelectrically shielded room (artifact free). The MP-based artifact\r\nremoval outperformed average artifact subtraction (AAS) and\r\noptimal basis set removal (OBS) in terms of restoring the EEG field\r\nmap topography of the VEP. Subsequently, a dipole model was fitted\r\nto the VEP under each condition using a realistic boundary element\r\nhead model. The source location of the VEP recorded inside the MR\r\nscanner was closest to that of the artifact free VEP after cleaning\r\nwith the MP-based algorithm as well as with AAS. While none of the\r\ntested algorithms offered complete removal, MP showed promising\r\nresults due to its ability to adapt to variations of latency, frequency\r\nand amplitude of individual artifact occurrences while still utilizing a\r\ncommon template.","references":"[1] S. Debener, C. Kranczioch, and I. Gutberlet, \"EEG Quality: Origin and\r\nReduction of the EEG Cardiac-Related Artefact,\" in EEG-fMRI:\r\nPhysiological Basis, Technique and Applications, C. Mulert and L.\r\nLemieux, Ed. Berlin: Springer, 2010, p. 539.\r\n[2] P. J. Allen, G. Polizzi, K. Krakow, D. R. Fish, et al., \"Identification of\r\nEEG events in the MR scanner: the problem of pulse artifact and a\r\nmethod for its subtraction,\" NeuroImage, vol. 8, pp. 229-39, 1998.\r\n[3] R. K. Niazy, C. F. Beckmann, G. D. Iannetti, J. M. Brady, and S. M.\r\nSmith, \"Removal of FMRI environment artifacts from EEG data using\r\noptimal basis sets,\" NeuroImage, vol. 28, pp. 720-37, 2005.\r\n[4] A. Delorme and S. Makeig, \"EEGLAB: an open source toolbox for\r\nanalysis of single-trial EEG dynamics including independent component\r\nanalysis,\" J Neurosci Methods, vol. 134, pp. 9-21, 2004.\r\n[5] M. Gratkowski, J. Haueisen, L. Arendt-Nielsen, A. C. Chen, and F.\r\nZanow, \"Decomposition of biomedical signals in spatial and timefrequency\r\nmodes,\" Methods Inf Med, vol 47, pp. 26-37, 2008.\r\n[6] M. Gratkowski, J. Haueisen, L. Arendt-Nielsen, A.C. Chen, and F.\r\nZanow,\" Time-frequency filtering of MEG signals with matching\r\npursuit\". J Physiol Paris, vol. 99, pp. 47-57, 2006.\r\n[7] H. Schimmel, \"The (+) reference: accuracy of estimated mean\r\ncomponents in average response studies,\" Science, vol. 157, pp. 92-4,\r\n1967.\r\n[8] J. W. Meijs, O. W. Weier, M. J. Peters, and A. van Oosterom, \"On the\r\nnumerical accuracy of the boundary element method,\" IEEE Trans\r\nBiomed Eng, vol 36, pp. 1038-49, 1989.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 56, 2011"}