TY - GEN
T1 - Visualization of multichannel EEG coherence networks based on community structure analysis
AU - Ji, Chengtao
AU - Maurits, Natasha M.
AU - Roerdink, Jos B.T.M.
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85036636524&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-72150-7_47
DO - 10.1007/978-3-319-72150-7_47
M3 - Conference Proceeding
AN - SCOPUS:85036636524
SN - 9783319721491
T3 - Studies in Computational Intelligence
SP - 583
EP - 594
BT - Complex Networks and Their Applications VI - Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications)
A2 - Cherifi, Hocine
A2 - Cherifi, Chantal
A2 - Musolesi, Mirco
A2 - Karsai, Márton
PB - Springer Verlag
T2 - 6th International Conference on Complex Networks and Their Applications, Complex Networks 2017
Y2 - 29 November 2017 through 1 December 2017
ER -