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Eye-tracking research has proven valuable in understanding numerous cognitivefunctions. Recently, Frey et al. provided an exciting deep learning method forlearning eye movements from fMRI data. However, it needed to co-register fMRIinto standard space to obtain eyeballs masks, and thus required additionaltemplates and was time consuming. To resolve this issue, in this paper, wepropose a framework named MRGazer for predicting eye gaze points from fMRI inindividual space. The MRGazer consisted of eyeballs extraction module and aresidual network-based eye gaze prediction. Compared to the previous method,the proposed framework skips the fMRI co-registration step, simplifies theprocessing protocol and achieves end-to-end eye gaze regression. The proposedmethod achieved superior performance in a variety of eye movement tasks thanthe co-registration-based method, and delivered objective results within ashorter time (~ 0.02 Seconds for each volume) than prior method (~0.3 Secondsfor each volume).

MRGazer: Decoding Eye Gaze Points from Functional Magnetic Resonance Imaging in Individual Space