EEG visualisation

Visualisation of multichannel EEG data is no trivial task. One tries to maximize the information gain obtained from multichannel time series without cluttering the screen with useless details. A nice trial can and some clear ideas can be found here. 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 introduce the concept of 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. To detect FUs, we developed a maximal clique based method, which is very time consuming, and a much more efficient watershed-based greedy method, thus making interactive visualization of multichannel EEG coherence possible. An example is shown in the figure below. Brain responses were collected from three subjects using an EEG cap with 119 scalp electrodes. During a so-called P300 experiment, each participant was instructed to count and report the number of (rare) target tones of 2000 Hz, alternated with standard tones of 1000 Hz which were to be ignored. To each electrode a cell is associated and all cells belonging to an FU have a corresponding color. Lines connect FU centers if the inter-FU coherence exceeds a significance threshold. The color of a line depends on the inter-FU coherence. Shown are FU maps for target stimuli data, with FUs larger than 5 cells, for the 1-3Hz EEG frequency band (top row) and for 13-20Hz (bottom row), for three datasets. Read on