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Magnetoencephalography and electroencephalography (M/EEG) are non-invasivemodalities that measure the weak electromagnetic fields generated by neuralactivity. Estimating the location and magnitude of the current sources thatgenerated these electromagnetic fields is a challenging ill-posed regressionproblem known as \emph{source imaging}. When considering a group study, acommon approach consists in carrying out the regression tasks independently foreach subject. An alternative is to jointly localize sources for all subjectstaken together, while enforcing some similarity between them. By pooling allmeasurements in a single multi-task regression, one makes the problem betterposed, offering the ability to identify more sources and with greaterprecision. The Minimum Wasserstein Estimates (MWE) promotes focal activationsthat do not perfectly overlap for all subjects, thanks to a regularizer basedon Optimal Transport (OT) metrics. MWE promotes spatial proximity on thecortical mantel while coping with the varying noise levels across subjects. Onrealistic simulations, MWE decreases the localization error by up to 4 mm persource compared to individual solutions. Experiments on the Cam-CAN datasetshow a considerable improvement in spatial specificity in population imaging.Our analysis of a multimodal dataset shows how multi-subject sourcelocalization closes the gap between MEG and fMRI for brain mapping.

Multi-subject MEG/EEG source imaging with sparse multi-task regression