Data-driven visualization and group analysis of multichannel EEG coherence with functional units
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1
University of Groningen, Netherlands
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.
To enable group comparisons, we also introduce two group maps. 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.
We applied our visualization method to two case studies, one on ageing and another one on mental fatigue. Results indicated that our approach overcomes the severe limitations of conventional hypothesis-driven methods which depend on previous investigations. Our method leads to a selection of coherences of interest taking full advantage of the recordings under investigation. Most importantly, the presented visualization of (group) FU maps provides a very economical data summary of massive amounts of experimental data, which otherwise would be very difficult and time-consuming to assess.
References
1. M. ten Caat, N.M. Maurits, and J.B.T.M. Roerdink. Data-driven visualization and group analysis of multichannel EEG coherence with functional units. IEEE Trans. Visualization and Computer Graphics, Vol. 14, no. 4, July/August 2008.
2. M. ten Caat, M.M. Lorist, E. Bezdan, J.B.T.M. Roerdink, N.M. Maurits. High-density EEG coherence analysis using functional units applied to mental fatigue. Journal of Neuroscience Methods, 2008, in press.
Conference:
Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008.
Presentation Type:
Poster Presentation
Topic:
Electrophysiology
Citation:
Roerdink
J,
Ten Caat
M,
Lorist
M,
Bezdan
E and
Maurits
N
(2008). Data-driven visualization and group analysis of multichannel EEG coherence with functional units.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2008.
doi: 10.3389/conf.neuro.11.2008.01.101
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Received:
28 Jul 2008;
Published Online:
28 Jul 2008.
*
Correspondence:
Jos Roerdink, University of Groningen, Groningen, Netherlands, j.b.t.m.roerdink@rug.nl