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

摘要

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 and a more efficient watershed based (WB) method. To reduce the potential over-segmentation of the WB method, we introduce here an improved watershed based (IWB) method. The WB and IWB method both are 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. We also introduce here two novel group maps for data-driven group analysis as extensions of the IWB method. Finally, we employ an extensive case study to evaluate the IWB FU map and the two new group maps for data-driven group analysis.