Keywords: Motion Correction, Motion Correction, EEG-fMRI
Motivation: Motion tracking in EEG-fMRI has been a challenging area of research in recent years, with existing approaches frequently suffering from limitations in spatial and temporal resolution or requiring additional hardware or calibration scans.
Goal(s): We aim to introduce a motion-tracking approach by modelling the gradient artifacts induced in EEG recordings during EEG-fMRI studies.
Approach: We introduce an algorithm tailored for detecting rigid and non-rigid head motion in EEG-fMRI data and aim to assess its performance by comparing it with camera-based motion detection techniques.
Results: We have demonstrated the capability of our algorithm to accurately identify motion in EEG-fMRI.
Impact: Our method shows potential across a range of uses, such as enhancing EEG data quality, especially in the context of reducing motion-related artifacts in EEG-fMRI studies.
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