Visualization of multichannel EEG coherence networks based on community structure analysis

Chengtao Ji*, Natasha M. Maurits, Jos B.T.M. Roerdink

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications VI - Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications)
EditorsHocine Cherifi, Chantal Cherifi, Mirco Musolesi, Márton Karsai
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319721491
Publication statusPublished - 2018
Externally publishedYes
Event6th International Conference on Complex Networks and Their Applications, Complex Networks 2017 - Lyon, France
Duration: 29 Nov 20171 Dec 2017

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


Conference6th International Conference on Complex Networks and Their Applications, Complex Networks 2017

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