Brain Topography

, Volume 28, Issue 6, pp 771–784

Real-Time MEG Source Localization Using Regional Clustering

  • Christoph Dinh
  • Daniel Strohmeier
  • Martin Luessi
  • Daniel Güllmar
  • Daniel Baumgarten
  • Jens Haueisen
  • Matti S. Hämäläinen
Original Paper

DOI: 10.1007/s10548-015-0431-9

Cite this article as:
Dinh, C., Strohmeier, D., Luessi, M. et al. Brain Topogr (2015) 28: 771. doi:10.1007/s10548-015-0431-9

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.

Keywords

Magnetoencephalography Real-time Source localization Minimum-norm estimates K-means clustering  Brain atlas 

Supplementary material

Supplementary material 1 (mp4 26 KB)

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Christoph Dinh
    • 1
    • 2
  • Daniel Strohmeier
    • 2
  • Martin Luessi
    • 1
  • Daniel Güllmar
    • 3
  • Daniel Baumgarten
    • 2
  • Jens Haueisen
    • 2
    • 4
  • Matti S. Hämäläinen
    • 1
  1. 1.Massachusetts General Hospital - Massachusetts Institute of Technology - Harvard Medical SchoolAthinoula A. Martinos Center for Biomedical ImagingCharlestownUSA
  2. 2.Institute of Biomedical Engineering and InformaticsTechnische Universität IlmenauIlmenauGermany
  3. 3.Medical Physics Group, Institute of Diagnostic and Interventional RadiologyFriedrich-Schiller-Universität JenaJenaGermany
  4. 4.Biomagnetic Center, Department of NeurologyFriedrich-Schiller-Universität JenaJenaGermany

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