Effective connectivity in the primary somatosensory network using combined EEG and MEGEffective connectivity in the primary somatosensory network using combined EEG and MEG Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2020-04-102019enThe primary somatosensory cortex remains one of the most investigated brain areas. However, there is still an absence of an integrated methodology to describe the early temporal alterations in the primary somatosensory network. Source analysis based on combined Electro-(EEG) and Magneto-(MEG) Encephalography (EMEG) has been recently shown to outperform the one's based on single modality EEG or MEG. The study and potential of combined EMEG form the goal of the current study, which investigates the time-variant connectivity of the primary somatosensory network. A subject-individualized pipeline combines a functional source separation approach with the effective connectivity analysis of different spatiotemporal source patterns using a realistic and skull-conductivity calibrated head model. Three-time windows are chosen for each modality EEG, MEG, and EMEG to highlight the thalamocortical and corticocortical interactions. The results show that EMEG is promising in suppressing a so-called connectivity 'leakage' effect when later components seem to influence earlier components, just due to too similar leadfields. Our current results support the notion that EMEG is superior in suppressing the spurious flows within a network of very rapid alterations.http://creativecommons.org/licenses/by/4.0/593-59719th International Conference on Bioinformatics and BioengineeringProceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering Politof Konstantinos Πολιτωφ Κωνσταντινος Antonakakis Marios Αντωνακακης Μαριος Wollbrink Andreas Zervakis Michail Ζερβακης Μιχαηλ Wolters Carsten Hermann Institute of Electrical and Electronics Engineers Connectivity Analysis Electroencephalography Finite element modeling Functional Source Separation Magnetoencephalography Primary somatosensory cortex Source Analysis