Natasha Maurits - Visualization of complex neurophysiological data: background, methods and applications

After introducing myself and my field of research, I would like to present the results of a fruitful multidisciplinary collaboration between Prof. Roerdink’s group of Scientific Visualization and Computer Graphics (RuG) and myself concerning data-driven analysis and visualization of high-dimensional brain data, resulting from 128-channel electroencephalography (EEG) recordings.

Synchronous electrical (EEG) activity in different brain regions is generally assumed to imply functional relationships between these regions. A measure for this synchrony is EEG coherence, calculated between pairs of electrode signals as a function of frequency. A typical data-driven visualization of 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 defined a functional unit (FU) as a data-driven region of interest. Different methods were studied to identify FUs, taking neurophysiological principles into account. I will here discuss these methods and extensions to allow within- and between-group analysis. Finally, the application of these methods to the study of mental fatigue and neurodegenerative disease will be explained.