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

Abstract-- 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(3n/3 ), with n the number of vertices) and a more efficient watershed based (WB) method (time complexity O(n2 log n)). To reduce the potential over-segmentation of the WB method, we introduce an improved watershed based (IWB) method (time complexity O(n2 log n)). The IWB method merges basins representing FUs during the segmentation if they are...

• Type: Journal
• Year: 2008
• Where: TVCG
• Authors: Michael ten Caat, Natasha M. Maurits, Jos B. T. M. Roerdink
• Keywords: Electrode, Multichannel Eeg, Time Complexity, TVCG 2008