Keywords: Machine Learning/Artificial Intelligence, fMRI, eye movementEye movements reflect changes in human behavior and thought to some extent, but many functional magnetic resonance imaging (fMRI) studies are limited by equipment and do not perform eye movement tracking. Recently, a deep learning method has been proposed for the regression problem of a single volume's gaze point. In this paper, we propose an end-to-end pipeline called MRGazerⅡ, which includes eye signal extraction, eye-movement behavior recognition and gaze point regression from fMRI scanning slices. The method was tested on the human connectome project (HCP) fMRI dataset and achieved the desired performance.
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