Visual Exploration of Dynamic Multichannel EEG Coherence Networks
| dc.contributor.author | Ji, C. | en_US |
| dc.contributor.author | Gronde, J. J. | en_US |
| dc.contributor.author | Maurits, N. M. | en_US |
| dc.contributor.author | Roerdink, J. B. T. M. | en_US |
| dc.contributor.editor | Chen, Min and Benes, Bedrich | en_US |
| dc.date.accessioned | 2019-03-17T09:57:02Z | |
| dc.date.available | 2019-03-17T09:57:02Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing 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 time window. In addition, we introduce the time‐annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. 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 a first step before a complete analysis of dynamic EEG coherence networks.Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing 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 time window. In addition, we introduce the time‐annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. 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 a first step before a complete analysis of dynamic EEG coherence networks. | en_US |
| dc.description.number | 1 | |
| dc.description.sectionheaders | Articles | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 38 | |
| dc.identifier.doi | 10.1111/cgf.13588 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 507-520 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13588 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13588 | |
| dc.publisher | © 2019 The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | information visualization | |
| dc.subject | visualization | |
| dc.subject | medical imaging | |
| dc.subject | visualization | |
| dc.subject | •Applied Computing → Life and Medical Sciences | |
| dc.subject | •Human‐Centered Computing → Information Visualization | |
| dc.title | Visual Exploration of Dynamic Multichannel EEG Coherence Networks | en_US |