We propose a method for correcting gradient artefacts in simultaneous EEG-FMRI that are variable from shot-to-shot, where artefacts cannot be identified via averaging. The artefact model is extracted from a data-driven decomposition that identifies the signal contributions which show geometric variation matching that of the trajectory rotation model. We show that this correction, applied to a rotating EPI trajectory, works just as well as standard approaches applied to conventionally sampled (non-rotating) EPI data. This will allow the use of more flexible sampling approaches in simultaneous EEG/FMRI that facilitate highly accelerated dynamic image reconstruction.
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