Data-driven cortex parcelling: a regularization tool for the EEG/MEG inverse problem

Recent inverse approaches based on the distributed source model require the use of functionally coherent cortical regions. In this note, we present an EEG/MEG data-driven method for parceling the cortical surface into a set of connex and functionally coherent components. We therefore consider the realistic tridimensional geometry of the cortical sheet and define functional coherence criteria that rely upon the recently proposed multivariate source prelocalization (MSP). We also describe an automatic way of estimating the optimal parcelling hyperparameter, given the EEG/MEG measurements. This new approach leads to a restricted and functionally meaningful description of the inverse solution space, which might be further exploited for constraining the source reconstruction process itself.