Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is extensively applied for brain mapping due to the high temporal resolution of EEG and high spatial resolution of fMRI. But gradient artifacts on the EEG cannot be optimally corrected in the presence of abrupt head movements. In this work, we demonstrate a method to model motion-related gradient artifacts. Thus we obtain not only an improvement in gradient artifact correction, but also infer motion information directly from the EEG.
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