  Advances on signal, image and video generation underly major breakthroughs on
generative medical imaging tasks, including Brain Image Synthesis. Still, the
extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped
from the brain electrophysiology remains largely unexplored. This work provides
the first comprehensive view on how to use state-of-the-art principles from
Neural Processing to synthesize fMRI data from electroencephalographic (EEG)
data. Given the distinct spatiotemporal nature of haemodynamic and
electrophysiological signals, this problem is formulated as the task of
learning a mapping function between multivariate time series with highly
dissimilar structures. A comparison of state-of-the-art synthesis approaches,
including Autoencoders, Generative Adversarial Networks and Pairwise Learning,
is undertaken. Results highlight the feasibility of EEG to fMRI brain image
mappings, pinpointing the role of current advances in Machine Learning and
showing the relevance of upcoming contributions to further improve performance.
EEG to fMRI synthesis offers a way to enhance and augment brain image data, and
guarantee access to more affordable, portable and long-lasting protocols of
brain activity monitoring. The code used in this manuscript is available in
Github and the datasets are open source.
