Zeffiro Interface (ZI), © 2018- Sampsa Pursiainen & ZI Development Team,
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). 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 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
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
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.
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Zeffiro正反向电磁脑成像仿真matlab代码.zip
共655个文件
m:519个
mlapp:31个
asv:21个
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Zeffiro正反向电磁脑成像仿真matlab代码.zip (655个子文件)
rh_white.asc 9.81MB
rh_pial.asc 9.81MB
lh_white.asc 9.76MB
lh_pial.asc 9.75MB
rh_CerebellumCortex.asc 1.71MB
lh_CerebellumCortex.asc 1.61MB
plot_meshes.asv 59KB
plot_volume.asv 35KB
zef_find_mne_reconstruction.asv 10KB
zef_ES_optimize_current.asv 8KB
zef_update.asv 7KB
zef_reconstructionTool_start.asv 4KB
zef_init_options.asv 3KB
zef_reconstructionTool_apply.asv 3KB
zef_dataBank_reorderTree.asv 3KB
LeadFieldProcessingTool_start.asv 3KB
zef_dataBank_combineLeadFields.asv 2KB
zef_LeadfieldProcessingTool_aux2current.asv 2KB
zef_create_sensors.asv 2KB
zef_LeadfieldProcessingTool_updateTable.asv 1KB
zef_update_transform.asv 1KB
zef_ES_clear_plot_data.asv 1KB
zef_reconstructionTool_replace.asv 1KB
zef_dataBank_delete.asv 409B
zef_slidding_callback.asv 406B
zef_dataBank_init.asv 243B
zef_LeadfieldProcessingTool_delete.asv 153B
time_lapse.avi 16.66MB
outer_skin_triangles.dat 980KB
outer_skull_triangles.dat 980KB
outer_skin_points.dat 490KB
outer_skull_points.dat 490KB
inner_skull_points.dat 359KB
inner_skull_triangles.dat 335KB
cem_electrodes.dat 6KB
sensors.dat 5KB
directions.dat 5KB
.DS_Store 6KB
.DS_Store 6KB
zeffiro_interface_segmentation_tool.fig 12.57MB
zeffiro_interface_figure_tool.fig 12.51MB
additional_options.fig 55KB
hb_sampler.fig 50KB
zeffiro_interface_mesh_tool.fig 39KB
exp_em_map_estimation_multires.fig 39KB
exp_ias_map_estimation_multires.fig 38KB
ias_map_estimation_roi.fig 36KB
ramus_sampler.fig 33KB
zeffiro_interface_ramus_inversion_tool.fig 30KB
zeffiro_interface_ramus_inversion_tool.fig 27KB
exp_em_map_estimation.fig 27KB
exp_ias_map_estimation.fig 27KB
zeffiro_interface_parcellation_tool.fig 24KB
ias_map_estimation.fig 22KB
find_synthetic_source.fig 21KB
zeffiro_interface_topography.fig 20KB
zef_mne_tool.fig 20KB
find_synthetic_eit_data.fig 19KB
zeffiro_interface_butterfly_plot.fig 16KB
zeffiro_plugins.ini 2KB
zeffiro_interface.ini 363B
LICENSE 34KB
debug.log 67B
print_meshes.m 97KB
plot_meshes.m 61KB
plot_meshes_proto.m 57KB
plot_volume.m 36KB
zef_eit_sensitivity_tool_substitute.m 29KB
lead_field_eeg_fem.m 29KB
lead_field_tes_fem.m 29KB
lead_field_meg_grad_fem.m 28KB
lead_field_meg_fem.m 27KB
zef_beamformer.m 26KB
MUSIC_iteration.m 21KB
lead_field_eit_fem.m 20KB
switch_onoff.m 20KB
zef_sigma.m 18KB
compute_eit_data.m 18KB
zeffiro_interface_figure_tool.m 15KB
zef_ES_plot_current_pattern.m 14KB
zeffiro_interface_segmentation_tool.m 13KB
ias_iteration_roi.m 13KB
zef_PlotGMModel.m 12KB
zef_import_segmentation.m 12KB
zef_GMModeling.m 11KB
zef_parcellation_time_series.m 11KB
zef_init.m 11KB
ramus_sampling_process.m 11KB
GMModelApp_start.m 11KB
zef_dpq_plot_GMM.m 10KB
zef_find_mne_reconstruction.m 10KB
zef_dpq_plot_GMM_v1.m 10KB
zef_PlotGMMcluster.m 10KB
mcmc_sampler.m 9KB
zef_update.m 9KB
exp_em_iteration_multires.m 9KB
zef_ES_find_currents.m 9KB
zeffiro_interface.m 9KB
zef_GMModeling_K.m 8KB
zef_ES_optimize_current.m 8KB
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