title: Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging.
abstract: Electroencephalography (EEG) and magnetoencephalography (MEG) have different sensitivities to differently configured brain activations, making them complimentary in providing independent information for better detection and inverse reconstruction of brain sources. In the present study, we developed an integrative approach, which integrates a novel sparse electromagnetic source imaging method, i.e., variation-based cortical current density (VB-SCCD), together with the combined use of EEG and MEG data in reconstructing complex brain activity. To perform simultaneous analysis of multimodal data, we proposed to normalize EEG and MEG signals according to their individual noise levels to create unit-free measures. Our Monte Carlo simulations demonstrated that this integrative approach is capable of reconstructing complex cortical brain activations (up to 10 simultaneously activated and randomly located sources). Results from experimental data showed that complex brain activations evoked in a face recognition task were successfully reconstructed using the integrative approach, which were consistent with other research findings and validated by independent data from functional magnetic resonance imaging using the same stimulus protocol. Reconstructed cortical brain activations from both simulations and experimental data provided precise source localizations as well as accurate spatial extents of localized sources. In comparison with studies using EEG or MEG alone, the performance of cortical source reconstructions using combined EEG and MEG was significantly improved. We demonstrated that this new sparse ESI methodology with integrated analysis of EEG and MEG data could accurately probe spatiotemporal processes of complex human brain activations. This is promising for noninvasively studying large-scale brain networks of high clinical and scientific significance.
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA. leiding@ou.edu
authors.forename: Lei
authors.initials: L
authors.lastname: Ding
authors.forename: Han
authors.initials: H
authors.lastname: Yuan
mesh_terms: D019540:Area Under Curve; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography; D055590:Electromagnetic Phenomena; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography; D008959:Models, Neurological; D009010:Monte Carlo Method
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