Zeffiro Interface (ZI), © 2018- Sampsa Pursiainen & ZI Development Team, is an open source code package constituting an accessible tool for multidisciplinary finite element (FE) based forward and inverse simulations in complex geometries. Install ZI using zeffiro_downloader.m to allow automatic updates between the local and remote repositories. The installer and then ZI can be obtained on matlab's command line as follows:
urlwrite('https://tinyurl.com/zeffiro','zeffiro_downloader.m'); zeffiro_downloader;
where the URL is a shortcut to the page:
https://raw.githubusercontent.com/sampsapursiainen/zeffiro_interface/master/zeffiro_downloader.m
With ZI, one can can generate a volumetric finite element mesh for a realistic multilayer geometry, if triangular surface grids in STL, DAT or ASC (ASCII) file format are available. A suitable surface segmentation can be produced, for example, using the FreeSurfer software suite (Copyright © FreeSurfer, 2013). Such a segmentation can be imported at once from a folder containing a set of ASCII files. An example folder can be found in the repository. 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 finite element 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 found at:
https://github.com/sampsapursiainen/zeffiro_interface/wiki
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, doi:10.1007/s12021-019-09436-9
For recent papers, see:
Galaz Prieto, F., Rezaei, A., Samavaki, M., & Pursiainen, S. (2022). L1-norm vs. L2-norm fitting in optimizing focal multi-channel tES stimulation: linear and semidefinite programming vs. weighted least squares. Computer Methods and Programs in Biomedicine, 226, 107084, https://doi.org/10.1016/j.cmpb.2022.107084
Lahtinen, J., Koulouri, A., Rezaei, A., & Pursiainen, S. (2022). Conditionally Exponential Prior in Focal Near-and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS). Journal of Mathematical Imaging and Vision, 1-22.
Rezaei, A., Lahtinen, J., Neugebauer, F., Antonakakis, M., Piastra, M. C., Koulouri, A., Wolters, C. H., & Pursiainen, S. (2021). Reconstructing subcortical and cortical somatosensory activity via the RAMUS inverse source analysis technique using median nerve SEP data. NeuroImage, 245, 118726.
Rezaei, A., Koulouri, A., & Pursiainen, S. (2020). Randomized multiresolution scanning in focal and fast E/MEG sensing of brain activity with a variable depth. Brain Topography, 33(2), 161-175.
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 and the hierarchical Bayesian sampler are 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), 8502.
ZI is not designed to be used in clinical applications. The authors do not take the responsibility of the results obtained with ZI using clinical data.