Researcher: Chengtao Ji, former PhD student

Thesis defense: October 15, 2018

First promotor: Prof. Jos Roerdink, PhD (Computer Sciences, RuG)

Collaborators: Jasper van de Gronde, PhD

Funding: CSC

The brain is the most complicated organ of our body. Modern imaging techniques provide a way to help us to understand mechanisms of brain function underlying human behaviour. One direction of studying these data is to analyze synchrony properties among activities from different brain areas under various conditions. Electroencephalography (EEG) is a technique which is used to measure electric brain potentials under certain conditions. An EEG coherence network may then be constructed based on the obtained EEG signals, where coherence is a measure of the degree of synchrony between EEG signals. However, at the start of a scientific investigation, we usually do not know what kind of information (features) about the data can be useful for further study, and in that case the existing analytical methods are not suitable for the data at hand. For these cases, first visually exploring all the available data could give us an impression of striking patterns or deviations in the data. These observations can then help researchers to propose detailed hypotheses about the data. However, due to the complexity of the data at hand, most existing visualization methods used for a particular task or situation cannot be easily generalized to other cases. Therefore, the visual data exploration should include the context of the visualized structures and take into account requirements from domain experts. Based on this, this thesis provides a number of visualization methods to help researchers analyze both static and dynamic EEG coherence networks.

References

• Ji, C., van de Gronde, J. J., Maurits, N. M., & Roerdink, J. B. T. M. (2019). Visual Exploration of Dynamic Multichannel EEG Coherence Networks. COMPUTER GRAPHICS FORUM, 38(1), 507-520. https://doi.org/10.1111/cgf.13588
• Ji, C., Maurits, N. M., & Roerdink, J. B. T. M. (2018). Data-driven visualization of multichannel EEG coherence networks based on community structure analysis. Applied Network Science, 3(41). https://doi.org/(…)07/s41109-018-0096-x
• Visualization of Multichannel EEG Coherence Networks Based on Community Structure C. Ji, N.M. Maurits, J.B.T.M. Roerdink
  In: C. Cherifi et al. (eds.), Complex Networks & Their Applications VI, Studies in Computational Intelligence 689, https://doi.org/10.1007/978-3-319-72150-7_47, Springer International Publishing AG 2018
• Visualizing and Exploring Dynamic Multichannel EEG Coherence Networks. Chengtao Ji, Jasper van de Gronde, Natasha Maurits, Jos Roerdink. Eurographics Workshop on Visual Computing for Biology and Medicine (2017), S. Bruckner, A. Hennemuth, and B. Kainz (Editors), DOI: 10.2312/vcbm.20171238, digilib.eg.org.
• Chengtao Ji, Jasper J. van de Gronde, Natasha M. Maurits, and Jos B. T. M. Roerdink. Tracking and Visualizing Dynamic Structures in Multichannel EEG Coherence Networks. In EuroVis Poster. 2016.