title: Wedge MUSIC: a novel approach to examine experimental differences of brain source connectivity patterns from EEG/MEG data.
abstract: We introduce a novel method to estimate bivariate synchronization, i.e. interacting brain sources at a specific frequency or band, from MEG or EEG data robust to artifacts of volume conduction. The data driven calculation is solely based on the imaginary part of the cross-spectrum as opposed to the imaginary part of coherency. In principle, the method quantifies how strong a synchronization between a distinct pair of brain sources is present in the data. As an input of the method all pairs of pre-defined locations inside the brain can be used which is computationally exhaustive. In contrast to that, reference sources can be used that have been identified by any source reconstruction technique in a prior analysis step. We introduce different variants of the method and evaluate the performance in simulations. As a particular advantage of the proposed methodology, we demonstrate that the novel approach is capable of investigating differences in brain source interactions between experimental conditions or with respect to a certain baseline. For measured data, we first show the application on resting state MEG data where we find locally synchronized sources in the motor-cortex based on the sensorimotor idle rhythms. Finally, we show an example on EEG motor imagery data where we contrast hand and foot movements. Here, we also find local interactions in the expected brain areas.
authors.affiliation: Dept. of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Berlin Institute of Technology, Machine Learning Laboratory, Franklinstr 28/29, 10587 Berlin, Germany; NIRx Medizintechnik GmbH, Baumbachstr. 17, 13189 Berlin, Germany. Electronic address: mail@aewald.net.
authors.forename: Arne
authors.initials: A
authors.lastname: Ewald
authors.affiliation: Fraunhofer FOKUS, Kaiserin Augusta Allee. 31, 10589 Berlin, Germany; Berlin Institute of Technology, Machine Learning Laboratory, Franklinstr 28/29, 10587 Berlin, Germany.
authors.forename: Forooz Shahbazi
authors.initials: FS
authors.lastname: Avarvand
authors.affiliation: Dept. of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany.
authors.forename: Guido
authors.initials: G
authors.lastname: Nolte
mesh_terms: D000328:Adult; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D009044:Motor Cortex / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted
keywords: Class differences; Electroencephalography; Functional connectivity; Imaginary part of coherency; Magnetoencephalography; Source localization; Volume conduction