Graph drawing by force-directed placement
Software—Practice & Experience
Choosing effective colours for data visualization
Proceedings of the 7th conference on Visualization '96
Journal of Combinatorial Theory Series B - Special issue: dedicated to Professor W. T. Tutte on the occasion of his eightieth birthday
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Software Components Capture Using Graph Clustering
IWPC '03 Proceedings of the 11th IEEE International Workshop on Program Comprehension
Density Functions for Visual Attributes and Effective Partitioning in Graph Visualization
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Design and Evaluation of Tiled Parallel Coordinate Visualization of Multichannel EEG Data
IEEE Transactions on Visualization and Computer Graphics
The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
Graphs, Networks and Algorithms
Graphs, Networks and Algorithms
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
Tiled parallel coordinates for the visualization of time-varying multichannel EEG data
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
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Synchronous electrical activity in different brain regions is generally assumed to imply functional relationships between these regions. A measure for this synchrony is electroencephalography (EEG) coherence, computed between pairs of signals as a function of frequency. Existing high-density EEG coherence visualizations are generally either hypothesis-driven, or data-driven graph visualizations which are cluttered. In this paper, a new method is presented for data-driven visualization of high-density EEG coherence, which strongly reduces clutter and is referred to as functional unit (FU) map. Starting from an initial graph, with vertices representing electrodes and edges representing significant coherences between electrode signals, we define an FU as a set of electrodes represented by a clique consisting of spatially connected vertices. In an FU map, the spatial relationship between electrodes is preserved, and all electrodes in one FU are assigned an identical gray value. Adjacent FUs are visualized with different gray values and FUs are connected by a line if the average coherence between FUs exceeds a threshold. Results obtained with our visualization are in accordance with known electrophysiological findings. FU maps can be used as a preprocessing step for conventional analysis.