The WWU DUNEuro reference data set for combined EEG/MEG source analysis

The provided dataset consists of two high-quality realistic head models and combined EEG/MEG data which can be used for state-of-the-art methods in brain research, such as modern finite element methods (FEM) to compute the EEG/MEG forward problems using the software toolbox DUNEuro (http://duneuro.org).\n\nA combined EEG/MEG dataset from a somatosensory experiment is provided (sep_sef.zip): Somatosensory evoked potentials (SEP) and fields (SEF) were elicited by stimulating the median nerve at the wrist of the right arm with monophasic square-wave electrical pulses with 0.5 ms duration. A random stimulus onset asynchrony between 350 and 450 ms was used and the strength was adjusted to invoke a clear movement of the thumb. The duration of the experiment was 10 minutes for a measurement of 1200 trials and data was acquired with a sampling rate of 1200 Hz and online low pass filtered at 300 Hz. An artifact reduction was achieved by reversing the polarity of the stimulation during the second half of the measurement. A 74-channel EEG (EASYCAP GmbH, Herrsching, Germany), for which the electrode positions were digitized using a Polhemus device (FASTRAK, Polhemus Incorporated, Colchester, Vermont, U.S.A.), and a whole-head MEG with 275 axial gradiometers and 29 reference coils (OMEGA2005, VSM MedTech Ltd., Canada) were used in the measurement.\n\nEthics Statement: One healthy subject (49 years, male) participated in this study. The subject had no history of psychiatric or neurological disorders and had given written informed consent before the experiment. All procedures had been approved by the ethics committee of the University of Erlangen, Faculty of Medicine on 10.05.2011 (Ref. No. 4453).\n\nAdditionally, two different advanced realistic head models are supplied, which both use a six-compartment segmentation from T1/T2-MRI of the test subject. They differentiate between scalp, skull compacta, skull spongiosa, cerebrospinal fluid (CSF) and gray and white matter tissue. One head model is a tetrahedral volumetric mesh (realistic_tet_mesh_6c.msh), while the other provides the geometric information by level-sets for each tissue boundary (realistic_levelsets_6c.zip). \n\nA detailed description of the construction of the tetrahedral mesh can be found here (subsection 2.3), the main steps are presented in the following. First, the MR images were co-registered and resampled so that the voxels of the anatomical data are cubic. Furthermore, the images were cut sufficiently below the skull of the participant. Subsequently, the segmentation of the T1w and T2w was performed in order to create six volumetric masks representing the six tissue compartments. The brain compartment was segmented via the FreeSurfer software. The remaining preprocessing and creation of the volumetric masks was entirely performed via routines available in FieldTrip. In particular, the scalp and skull segmentations were done via the spm12 software, embedded in FieldTrip. Once the masks were assembled, a volumetric tetrahedral mesh was created using the CGAL software embedded in iso2mesh, resulting in 885,214 nodes and 5,335,615 tetrahedrons. The mesh is provided in gmsh format, including information about the node positions, elements defined by their node indices, and labels for each element indicating the tissue compartment.\n\nFor the construction of the unfitted head model, a six-compartment voxel segmentation was constructed based on the T1- and T2-weighted MR images, distinguishing between skin, skull compacta and spongiosa, CSF, gray and white matter using SPM12 via Fieldtrip, FSL and internal MATLAB routines. Surfaces were extracted from this voxel segmentation to distinguish between the different tissue compartments. In order to smooth the surfaces while sustaining the available information from the segmentation, we applied an anti-aliasing algorithm created for binary voxel images presented in (Whitaker, 2000). The resulting smoothed surfaces are represented as discrete level-set functions, i.e., by \\(N^3\\)-dimensional arrays (\\(N\\)=257), the value on each node indicates the signed distance to the respective surface.

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