Visualizing and Exploring Dynamic Multichannel EEG Coherence Networks

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dc.contributor.authorJi, Chengtaoen_US
dc.contributor.authorGronde, Jasper J. van deen_US
dc.contributor.authorMaurits, Natasha M.en_US
dc.contributor.authorRoerdink, Jos B. T. M.en_US
dc.contributor.editorStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Riederen_US
dc.date.accessioned2017-09-06T07:12:30Z
dc.date.available2017-09-06T07:12:30Z
dc.date.issued2017
dc.description.abstractAn electroencephalography (EEG) coherence network represents functional brain connectivity, and is constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of coherence networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline-based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole timewindow. In addition, we modified the FU map representation to facilitate comparison of the behavior of nodes between consecutive FU maps. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as an preprocessing step before a complete analysis of dynamic EEG coherence networks.en_US
dc.description.sectionheadersExploration and Visual Analysis
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20171238
dc.identifier.isbn978-3-03868-036-9
dc.identifier.issn2070-5786
dc.identifier.pages63-72
dc.identifier.urihttps://doi.org/10.2312/vcbm.20171238
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20171238
dc.publisherThe Eurographics Associationen_US
dc.subjectCCS Concepts
dc.subjectApplied computing
dc.subjectLife and medical sciences
dc.subjectHuman
dc.subjectcentered computing
dc.subjectInformation visualization
dc.titleVisualizing and Exploring Dynamic Multichannel EEG Coherence Networksen_US
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VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine