Abstract
Magnetoencephalography (MEG) signals are commonly contaminated by cardiac artefacts (CAs). Principle component analysis and independent component analysis have been widely used for removing CAs, but they typically require a complex procedure for the identification of CA-related components. We propose a simple and efficient method, resampled moving average subtraction (RMAS), to remove CAs from MEG data. Based on an electrocardiogram (ECG) channel, a template for each cardiac cycle was estimated by a weighted average of epochs of MEG data over consecutive cardiac cycles, combined with a resampling technique for accurate alignment of the time waveforms. The template was subtracted from the corresponding epoch of the MEG data. The resampling reduced distortions due to asynchrony between the cardiac cycle and the MEG sampling times. The RMAS method successfully suppressed CAs while preserving both event-related responses and high-frequency (>45 Hz) components in the MEG data.
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Acknowledgments
Experimental data were collected while Dr. -Ing. Sun worked in Max Planck Institute for brain research, Frankfurt am Main, Germany. This work was supported in part by the National Institutes of Health Grant NS037462 and by The National Center for Research Resources (P41RR14075).
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Sun, L., Ahlfors, S.P. & Hinrichs, H. Removing Cardiac Artefacts in Magnetoencephalography with Resampled Moving Average Subtraction. Brain Topogr 29, 783–790 (2016). https://doi.org/10.1007/s10548-016-0513-3
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DOI: https://doi.org/10.1007/s10548-016-0513-3