Graph drawing by force-directed placement
Software—Practice & Experience
Mean and maximum common subgraph of two graphs
Pattern Recognition Letters
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Weighted mean of a pair of graphs
Computing
GD '02 Revised Papers from the 10th International Symposium on Graph Drawing
Density Functions for Visual Attributes and Effective Partitioning in Graph Visualization
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units
IEEE Transactions on Visualization and Computer Graphics
EdgeLens: an interactive method for managing edge congestion in graphs
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
Graph averaging as a means to compare multichannel EEG coherence networks
EG VCBM'10 Proceedings of the 2nd Eurographics conference on Visual Computing for Biology and Medicine
Editorial: Special Section on Visual Computing in Biology and Medicine
Computers and Graphics
Mathematical morphology in computer graphics, scientific visualization and visual exploration
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
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A method is proposed for quantifying differences between multichannel EEG coherence networks represented by functional unit (FU) maps. The approach is based on inexact graph matching for attributed relational graphs and graph averaging, adapted to FU-maps. The mean of a set of input FU-maps is defined in such a way that it not only represents the mean group coherence during a certain task or condition but also to some extent displays individual variations in brain activity. The definition of a mean FU-map relies on a graph dissimilarity measure which takes into account both node positions and node or edge attributes. A visualization of the mean FU-map is used with a visual representation of the frequency of occurrence of nodes and edges in the input FUs. This makes it possible to investigate which brain regions are more commonly involved in a certain task, by analysing the occurrence of a FU of the mean graph in the input FUs. Furthermore, our method gives the possibility to quantitatively compare individual FU-maps by computing their distance to the mean FU-map. The method is applied to the analysis of EEG coherence networks in two case studies, one on mental fatigue and one on patients with corticobasal ganglionic degeneration (CBGD). The method is proposed as a preliminary step towards a complete quantitative comparison, and the real benefit of its application is still to be proven.