Zeffiro Interface (ZI), Sampsa Pursiainen © 2018, is an open source code
package constituting an accessible tool for finite element (FE) based
forward and inverse simulations in EEG/MEG and can be used also in other
bioelectromagnetical imaging applications targeting the brain. With ZI,
one can segment a realistic multilayer geometry and generate a
multi-compartment FE mesh, if triangular ASCII surface grids (in DAT or
ASC file format) are available. A suitable surface segmentation can be
produced, for example, with the FreeSurfer software suite (Copyright ©
FreeSurfer, 2013). ZI allows also importing a parcellation created with FreeSurfer
to enable distinguishing different brain regions and, thereby, analysing
the connectivity of the brain function over a time series. Different
compartments can be defined as active, allowing the analysis of the
sub-cortical strucures. In each compartment, the orientation of the
activity can be either normally constrained or unconstrained. The main
routines of ZI can be accelerated significantly in a computer equipped
with a graphics computing unit (GPU). It is especially recommendable to
perform the forward simulation process, i.e., to generate the FE mesh, the
lead field matrix and to interpolate between different point sets,
utilizing a GPU. After the forward simulation phase, the model can be
processed also without GPU acceleration.
A brief introduction to the essential features of the interface can be
The interface itself has been introduced in:
He, Q., Rezaei, A. & Pursiainen, S. (2019). Zeffiro User Interface for
Electromagnetic Brain Imaging: a GPU Accelerated FEM Tool for Forward
and Inverse Computations in Matlab. Neuroinformatics,
The essential mathematical techniques used in the interface have been
reviewed and validated in:
Miinalainen, T., Rezaei, A., Us, D., Nüßing, A., Engwer, C., Wolters, C.
H., & Pursiainen, S. (2019). A realistic, accurate and fast source
modeling approach for the EEG forward problem. NeuroImage, 184, 56-67.
Pursiainen, S. (2012). Raviart–Thomas-type sources adapted to applied EEG
and MEG: implementation and results. Inverse Problems, 28(6), 065013.
The IAS MAP (iterative alternating sequential maximum a posteriori)
inversion method is based on:
Calvetti, D., Hakula, H., Pursiainen, S., & Somersalo, E. (2009).
Conditionally Gaussian hypermodels for cerebral source localization. SIAM
Journal on Imaging Sciences, 2(3), 879-909.
It has been applied for a realistic brain geometry, e.g., in:
Lucka, F., Pursiainen, S., Burger, M., & Wolters, C. H. (2012).
Hierarchical Bayesian inference for the EEG inverse problem using
realistic FE head models: depth localization and source separation for
focal primary currents. Neuroimage, 61(4), 1364-1382.
The current preserving source model combines linear (face-intersecting)
and quadratic (edgewise) elements via the Position Based Optimization
(PBO) method and the 10-source stencil in which 4 face sources and 6 edge
sources are applied for each tetrahedral element containing a source:
Bauer, M., Pursiainen, S., Vorwerk, J., Köstler, H., & Wolters, C. H.
(2015). Comparison study for Whitney (Raviart–Thomas)-type source models
in finite-element-method-based EEG forward modeling. IEEE Transactions on
Biomedical Engineering, 62(11), 2648-2656.
Pursiainen, S., Vorwerk, J., & Wolters, C. H. (2016).
Electroencephalography (EEG) forward modeling via H (div) finite element
sources with focal interpolation. Physics in Medicine & Biology, 61(24),
Sampsa Pursiainen (2020). Zeffiro Forward and Inverse Interface for Brain Imaging (https://github.com/sampsapursiainen/zeffiro_interface), GitHub. Retrieved .
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