MEG/EEG reconstruction in the reduced source space

International audienceThe Electroencephalographiy (EEG) and Magnetoencephalography (MEG) are two non-invasive imaging modalities that measures the brain activity. Obtaining the brain activity with the distributed source model from these measurements is an ill-posed problem due to the high number of unknowns compared to the number of measurements. A unique solution is obtained by assuming a prior on the sources. The idea is to reduce the solution space size from the number of sources (S) to a smaller space. Assuming that sources inside each functional region have equal activation allows us to reduce the number of columns in the leadfield matrix from S to a number of regions (K). These regions are obtained from a dMRI parcellation-based region growing algorithm. A region is assumed to contain sources that have similar fibers distribution. To obtain a sparse solution, we assume that only a few regions are active simultaneously. BIC1 is used to obtain the optimal number of regions (Kp) that explains the MEG/EEG data. We compared the results of the proposed method to the ones from Minimum Norm Estimate (MNE) and LASSO2. The first gives a smooth solution and the second gives a sparse solution. To test the accuracy of the reconstruction, we activated simultaneously from two to five regions in both hemispheres with synthetic low SNR signals (10 dB). Our approach could detect the right number of activated regions and provided more accurate reconstructions compared to MNE and LASSO.Our approach assumes that few regions are active simultaneously which allows us to reduce the space to a few unkowns. It can be seen as an approximation to the l0 norm. Even though assuming a constant activation in each functional region is a hard constraint, but it allows us to reduce the space size from S to K. The obtained solution can be used to detect extended sources (e.g epileptic activity) or as an initialization step to other approaches to obtain focal solutions in the active regions