4.6 Article

DUNEuro-A software toolbox for forward modeling in bioelectromagnetism

Journal

PLOS ONE
Volume 16, Issue 6, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0252431

Keywords

-

Funding

  1. DFG through the Cluster of Excellence Cells in Motion [EXC 1003194347757]
  2. Cluster of Excellence Mathematics Munster: Dynamics Geometry - Structure [EXC 2044-390685587]
  3. DFG [WO1425/7-1, SPP1665, WO1425/5-2]
  4. EU project ChildBrain (Marie Curie Innovative Training Networks) [641652]
  5. Academy of Finland (AoF) [305055]
  6. Tampere University
  7. Centre of Excellence in Inverse Modelling and Imaging [AoF 336792]
  8. bilateral (MunsterTampere) mobility funding [AoF 326668]
  9. ERA-NET PerMed project PerEpi [AoF 344712]
  10. DAAD [57405052, 57523877]
  11. Austrian Wissenschaftsfonds (FWF) [I 3790-B27]
  12. AoF [317165, 334465]
  13. Academy of Finland (AKA) [305055, 305055] Funding Source: Academy of Finland (AKA)

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DUNEuro is a free and open-source C++ software toolbox designed for numerical computation of forward solutions in bioelectromagnetism using modern finite element methods. It offers various source models, extendable and user-friendly interfaces, integration with Python and MATLAB, as well as detailed installation instructions and example scripts for practical use.
Accurate and efficient source analysis in electro- and magnetoencephalography using sophisticated realistic head geometries requires advanced numerical approaches. This paper presents DUNEuro, a free and open-source C++ software toolbox for the numerical computation of forward solutions in bioelectromagnetism. Building upon the DUNE framework, it provides implementations of modern fitted and unfitted finite element methods to efficiently solve the forward problems of electro- and magnetoencephalography. The user can choose between a variety of different source models that are implemented. The software's aim is to provide interfaces that are extendable and easy-to-use. In order to enable a closer integration into existing analysis pipelines, interfaces to Python and MATLAB are provided. The practical use is demonstrated by a source analysis example of somatosensory evoked potentials using a realistic six-compartment head model. Detailed installation instructions and example scripts using spherical and realistic head models are appended.

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