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Brain Topography

, Volume 31, Issue 1, pp 125–128 | Cite as

Real-Time Clustered Multiple Signal Classification (RTC-MUSIC)

  • Christoph DinhEmail author
  • Lorenz Esch
  • Johannes Rühle
  • Steffen Bollmann
  • Daniel Güllmar
  • Daniel Baumgarten
  • Matti S. Hämäläinen
  • Jens Haueisen
Short Communication

Abstract

Magnetoencephalography (MEG) and electroencephalography provide a high temporal resolution, which allows estimation of the detailed time courses of neuronal activity. However, in real-time analysis of these data two major challenges must be handled: the low signal-to-noise ratio (SNR) and the limited time available for computations. In this work, we present real-time clustered multiple signal classification (RTC-MUSIC) a real-time source localization algorithm, which can handle low SNRs and can reduce the computational effort. It provides correlation information together with sparse source estimation results, which can, e.g., be used to identify evoked responses with high sensitivity. RTC-MUSIC clusters the forward solution based on an anatomical brain atlas and optimizes the scanning process inherent to MUSIC approaches. We evaluated RTC-MUSIC by analyzing MEG auditory and somatosensory data. The results demonstrate that the proposed method localizes sources reliably. For the auditory experiment the most dominant correlated source pair was located bilaterally in the superior temporal gyri. The highest activation in the somatosensory experiment was found in the contra-lateral primary somatosensory cortex.

Keywords

Real-time Source estimation RAP-MUSIC RTC-MUSIC Powell’s conjugate direction method K-means clustering 

Notes

Acknowledgements

This work was supported by the National Institutes of Health (NIH, Grants 4R01EB009048 and 5P41EB015896), European Union’s Horizon 2020 research and innovation program under grant agreement No 686865 the German Research Foundation (DFG, Grant Ba 4858/1-1), the Thuringian Ministry of Science under Grant Number 2015 FGR 0085 and the German Academic Exchange Service (DAAD).

Supplementary material

Supplementary material 1 (MP4 8609 KB)

10548_2017_586_MOESM2_ESM.docx (1.1 mb)
Supplementary material 2 (DOCX 1173 KB)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General Hospital - Massachusetts Institute of Technology - Harvard Medical SchoolCharlestownUSA
  2. 2.Institute of Biomedical Engineering and InformaticsTechnische Universität IlmenauIlmenauGermany
  3. 3.Centre for Advanced ImagingUniversity of QueenslandBrisbaneAustralia
  4. 4.Medical Physics Group, Institute of Diagnostic and Interventional RadiologyUniversity Hospital JenaJenaGermany
  5. 5.Institute of Electrical and Biomedical EngineeringUniversity of Health Sciences, Medical Informatics and Technology (UMIT)Hall in TirolAustria
  6. 6.Biomagnetic Center, Clinic for NeurologyJena University HospitalJenaGermany

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