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Visualization of Multichannel EEG Coherence Networks Based on Community Structure Analysis

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Complex Networks & Their Applications VI (COMPLEX NETWORKS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

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Abstract

An electroencephalography (EEG) coherence network is a representation of functional brain connectivity. However, typical visualizations of coherence networks do not allow an easy embedding of spatial information or suffer from visual clutter, especially for multichannel EEG coherence networks. In this paper, a new method for data-driven visualization of multichannel EEG coherence networks is proposed to avoid the drawbacks of conventional methods. This method partitions electrodes into dense groups of spatially connected regions. It not only preserves spatial relationships between regions, but also allows an analysis of the functional connectivity within and between brain regions, which could be used to explore the relationship between functional connectivity and underlying brain structures. In addition, we employ an example to illustrate the difference between the proposed method and two other data-driven methods when applied to coherence networks in older and younger adults who perform a cognitive task. The proposed method can serve as an preprocessing step before a more detailed analysis of EEG coherence networks.

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Acknowledgments

C. Ji acknowledges the China Scholarship Council (Grant number: 201406240159) for financial support.

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Authors and Affiliations

  1. Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands

    Chengtao Ji & Jos B. T. M. Roerdink

  2. Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    Natasha M. Maurits

Authors
  1. Chengtao Ji
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  2. Natasha M. Maurits
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  3. Jos B. T. M. Roerdink
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Corresponding author

Correspondence to Chengtao Ji .

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Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Ji, C., Maurits, N.M., Roerdink, J.B.T.M. (2018). Visualization of Multichannel EEG Coherence Networks Based on Community Structure Analysis. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_47

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72149-1

  • Online ISBN: 978-3-319-72150-7

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