Visualization and exploration of multichannel EEG coherence networks

Ji, C., 2018, [Groningen]: University of Groningen. 124 p.

Research output: ThesisThesis fully internal (DIV)Academic

Copy link to clipboard


  • Title and contents

    Final publisher's version, 433 KB, PDF document

  • Chapter 1

    Final publisher's version, 2 MB, PDF document

  • Chapter 2

    Final publisher's version, 11 MB, PDF document

  • Chapter 3

    Final publisher's version, 2 MB, PDF document

  • Chapter 4

    Final publisher's version, 1 MB, PDF document

  • Chapter 6

    Final publisher's version, 299 KB, PDF document

  • Bibliography

    Final publisher's version, 300 KB, PDF document

  • Acknowledgements

    Final publisher's version, 199 KB, PDF document

  • Complete thesis

    Final publisher's version, 17 MB, PDF document

  • Propositions

    Final publisher's version, 168 KB, PDF document

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.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
Award date15-Oct-2018
Place of Publication[Groningen]
Print ISBNs978-94-034-1056-2
Electronic ISBNs978-94-034-1077-7
Publication statusPublished - 2018

Download statistics

No data available

ID: 65388534