A System for Combined Visualization of EEG and Diffusion Tensor Imaging Tractography Data View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Alexander Wiebel , Cornelius Müller , Christoph Garth , Thomas R. Knösche

ABSTRACT

In this paper we present an interactive system that integrates the visual analysis of nerve fiber pathway approximations from diffusion tensor imaging (DTI) with electroencephalography (EEG) data. The technique uses source reconstructions from EEG data to define certain regions of interest in the brain. These regions, in turn, are used to selectively display subsets of the approximated fiber pathways in the brain. The selected pathways highlight potential connections from activated areas to other parts of the brain and can thus help to understand networks on which most higher brain function relies. Users can explore the neuronal network and activity by navigating in an EEG curve view. The navigation is supported by optional mechanisms like snapping to time points with present reconstructed dipoles and visual cues highlighting such points. To the best of our knowledge, the presented combination of time navigation in EEG curves together with DTI pathway selection at the corresponding dipole positions is new and has not been described before. The presented methods are freely available in an open source system for visualization and analysis in neuroscience. More... »

PAGES

325-337

References to SciGraph publications

  • 2004-06. ASA-Advanced Source Analysis of Continuous and Event-Related EEG/MEG Signals in BRAIN TOPOGRAPHY
  • 1992-12. Functional imaging and localization of electromagnetic brain activity in BRAIN TOPOGRAPHY
  • 2009. EEG Data Driven Animation and Its Application in COMPUTER VISION/COMPUTER GRAPHICS COLLABORATIONTECHNIQUES
  • Book

    TITLE

    Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data

    ISBN

    978-3-642-54300-5
    978-3-642-54301-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-54301-2_15

    DOI

    http://dx.doi.org/10.1007/978-3-642-54301-2_15

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1023419596


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