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