Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

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34 Citaten (Scopus)
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Samenvatting

A typical data- driven visualization of electroencephalography ( EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter, we define a functional unit ( FU) as a data- driven region of interest ( ROI). An FU is a spatially connected set of electrodes recording pairwise significantly coherent signals, represented in the coherence graph by a spatially connected clique. Earlier, we presented two methods to detect FUs: a maximal clique- based ( MCB) method ( time complexity O(3(n/3)), with n being the number of vertices) and a more efficient watershed- based ( WB) method ( time complexity O(n(2) log n)). To reduce the potential oversegmentation of the WB method, we introduce an improved WB ( IWB) method ( time complexity O(n(2) log n)). The IWB method merges basins representing FUs during the segmentation if they are spatially connected and if their union is a clique. The WB and IWB methods are both up to a factor of 100,000 faster than the MCB method for a typical multichannel setting with 128 EEG channels, thus making interactive visualization of multichannel EEG coherence possible. Results show that considering the MCB method as the gold standard, the difference between IWB and MCB FU maps is smaller than between WB and MCB FU maps. We also introduce two novel group maps for data- driven group analysis as extensions of the IWB method. First, the group mean coherence map preserves dominant features from a collection of individual FU maps. Second, the group FU size map visualizes the average FU size per electrode across a collection of individual FU maps. Finally, we employ an extensive case study to evaluate the IWB FU map and the two new group maps for data- driven group analysis. Results, in accordance with conventional findings, indicate differences in EEG coherence between younger and older adults. However, they also suggest that an initial selection of hypothesis- driven ROIs could be extended with additional data- driven ROIs.

Originele taal-2English
Pagina's (van-tot)756-771
Aantal pagina's16
TijdschriftIEEE Transactions on Visualization and Computer Graphics
Volume14
Nummer van het tijdschrift4
DOI's
StatusPublished - 2008
Evenement9th Eurographics/IEEE VGTC Symposium on Visualization (EuroVis '07) - , Sweden
Duur: 23-mei-200725-mei-2007

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