Visualization of Multichannel EEG Coherence Networks Based on Community Structure

An electroencephalography (EEG) coherence network is a representation of functional brain connectivity. However, typical visualizations of coherence networks do not allow an easy embedding of spatial information or suffer from visual clutter, especially for multichannel EEG coherence networks. In this paper, a new method for data-driven visualization of multichannel EEG coherence networks is proposed to avoid the drawbacks of conventional methods. This method partitions electrodes into dense groups of spatially connected regions. It not only preserves spatial relationships between regions, but also allows an analysis of the functional connectivity within and between brain regions, which could be used to explore the relationship between functional connectivity and underlying brain structures. In addition, we employ an example to illustrate the difference between the proposed method and two other data-driven methods when applied to coherence networks in older and younger adults who perform a cognitive task. The proposed method can serve as an preprocessing step before a more detailed analysis of EEG coherence networks

Tiled Parallel Coordinates for the Visualization of Time-Varying Multichannel EEG Data

The field of visualization assists data interpretation in many areas, but some types of data are not manageable by existing visualization techniques. This holds in particular for time-varying multichannel EEG data. No existing technique can simultaneously visualize information from all channels in use and all time steps. To address this problem, a new visualization technique is presented, based on the parallel coordinate method and making use of a tiled organization. This tiled organization employs a two-dimensional row-column representation, rather than a one-dimensional arrangement in columns as used for the classical parallel coordinates. The usefulness of the new method, referred to as tiled parallel coordinates, is demonstrated by one particular type of EEG data. It can be applied to an arbitrary number of time steps, for the maximum number of channels currently in use. The general setup of the method makes it widely applicable to other time-varying multivariate data types.