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


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.

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  1. Besserve M, Martinerie J, Garnero L (2011) Improving quantification of functional networks with EEG inverse problem: evidence from a decoding point of view. Neuroimage 55(4):1536–1547

    Article  PubMed  Google Scholar 

  2. Buch E, Weber C, Cohen LG, Braun C, a Dimyan M, Ard T, Mellinger J, Caria A, Soekadar S, Fourkas A, Birbaumer N (2008) Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39(3):910–917

    Article  PubMed  PubMed Central  Google Scholar 

  3. Congedo M, Lotte F, Lécuyer A (2006) Classification of movement intention by spatially filtered electromagnetic inverse solutions. Phys Med Biol 51(8):1971–1989

    CAS  Article  PubMed  Google Scholar 

  4. Dinh C, Luessi M, Sun L, Haueisen J, Hamalainen MS (2013) MNE-X: MEG/EEG real-time acquisition, real-time processing, and real-time source localization framework. Biomed Eng 58(1):4184

    Google Scholar 

  5. Dinh C, Strohmeier D, Luessi M, Güllmar D, Baumgarten D, Haueisen J, Hämäläinen MS (2015) Real-time MEG source localization using regional clustering. Brain Topogr 28:1–14

    Article  Google Scholar 

  6. Diwakar M, Tal O, Liu TT, Harrington DL, Srinivasan R, Muzzatti L, Song T, Theilmann RJ, Lee RR, Huang M-X (2011) Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer. Neuroimage 56(4):1918–1928

    Article  PubMed  Google Scholar 

  7. Jones SR, Kerr CE, Wan Q, Pritchett DL, Hamalainen MS, Moore CI (2010) Cued spatial attention drives functionally relevant modulation of the mu rhythm in primary somatosensory cortex. J Neurosci 30(41):13760–13765

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Lotte F, Lecuyer A, Arnaldi B (2009) FuRIA: an inverse solution based feature extraction algorithm using fuzzy set theory for brain-computer interfaces. IEEE Trans Signal Process 57(8):3253–3263

    Article  Google Scholar 

  9. Mosher JC, Leahy RM (1999) Source localization using recursively applied and projected (RAP) MUSIC. IEEE Trans Signal Process 47(2):332–340

    Article  Google Scholar 

  10. Noirhomme Q, Kitney RI, Macq B (2008) Single-trial EEG source reconstruction for brain-computer interface. IEEE Trans Biomed Eng 55(5):1592–1601

    Article  PubMed  Google Scholar 

  11. Qin L, Ding L, He B (2004) Motor imagery classification by means of source analysis for brain-computer interface applications. J Neural Eng 1(3):135–141

    Article  PubMed  Google Scholar 

  12. Sudre GP, Parkkonen L, Bock E, Baillet S, Wang W, Weber DJ (2011) rtMEG: a real-time software interface for magnetoencephalography. Comput Intell Neurosci 2011:327953

  13. Ziegler DA, Pritchett DL, Hosseini-Varnamkhasti P, Corkin S, Hamalainen MS, Moore CI, Jones SR (2010) Transformations in oscillatory activity and evoked responses in primary somatosensory cortex in middle age: a combined computational neural modeling and MEG study. Neuroimage 52(3):897–912

    Article  PubMed  PubMed Central  Google Scholar 

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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).

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Correspondence to Christoph Dinh.

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Dinh, C., Esch, L., Rühle, J. et al. Real-Time Clustered Multiple Signal Classification (RTC-MUSIC). Brain Topogr 31, 125–128 (2018).

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  • Real-time
  • Source estimation
  • Powell’s conjugate direction method
  • K-means clustering