title: Real-Time MEG Source Localization Using Regional Clustering.
abstract: With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject's reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements.
authors.affiliation: Massachusetts General Hospital - Massachusetts Institute of Technology - Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St., Charlestown, MA, 02129, USA. chdinh@nmr.mgh.harvard.edu.|Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Gustav-Kirchhoff-Str. 2, 98693, Ilmenau, Germany. chdinh@nmr.mgh.harvard.edu.
authors.forename: Christoph
authors.initials: C
authors.lastname: Dinh
authors.affiliation: Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Gustav-Kirchhoff-Str. 2, 98693, Ilmenau, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Strohmeier
authors.affiliation: Massachusetts General Hospital - Massachusetts Institute of Technology - Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St., Charlestown, MA, 02129, USA.
authors.forename: Martin
authors.initials: M
authors.lastname: Luessi
authors.affiliation: Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Friedrich-Schiller-Universität Jena, Philosophenweg 3, 07743, Jena, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Güllmar
authors.affiliation: Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Gustav-Kirchhoff-Str. 2, 98693, Ilmenau, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Baumgarten
authors.affiliation: Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Gustav-Kirchhoff-Str. 2, 98693, Ilmenau, Germany.|Biomagnetic Center, Department of Neurology, Friedrich-Schiller-Universität Jena, Erlanger Allee 101, 07743, Jena, Germany.
authors.forename: Jens
authors.initials: J
authors.lastname: Haueisen
authors.affiliation: Massachusetts General Hospital - Massachusetts Institute of Technology - Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St., Charlestown, MA, 02129, USA.
authors.forename: Matti S
authors.initials: MS
authors.lastname: Hämäläinen
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping; D016000:Cluster Analysis; D006801:Humans; D015225:Magnetoencephalography; D012815:Signal Processing, Computer-Assisted
keywords: Brain atlas; K-means clustering; Magnetoencephalography; Minimum-norm estimates; Real-time; Source localization