  Magnetoencephalography and electroencephalography (M/EEG) are non-invasive
modalities that measure the weak electromagnetic fields generated by neural
activity. Estimating the location and magnitude of the current sources that
generated these electromagnetic fields is a challenging ill-posed regression
problem known as \emph{source imaging}. When considering a group study, a
common approach consists in carrying out the regression tasks independently for
each subject. An alternative is to jointly localize sources for all subjects
taken together, while enforcing some similarity between them. By pooling all
measurements in a single multi-task regression, one makes the problem better
posed, offering the ability to identify more sources and with greater
precision. The Minimum Wasserstein Estimates (MWE) promotes focal activations
that do not perfectly overlap for all subjects, thanks to a regularizer based
on Optimal Transport (OT) metrics. MWE promotes spatial proximity on the
cortical mantel while coping with the varying noise levels across subjects. On
realistic simulations, MWE decreases the localization error by up to 4 mm per
source compared to individual solutions. Experiments on the Cam-CAN dataset
show a considerable improvement in spatial specificity in population imaging.
Our analysis of a multimodal dataset shows how multi-subject source
localization closes the gap between MEG and fMRI for brain mapping.
