This pipeline aims to create personalized solutions to the EEG/MEG forward problem including anatomical information from magnetic resonance imaging (MRI) and somatosensory evoked potentials/fields. T1-/T2-weighted MRI images are used to create a head model that includes 6 compartments (gray and white matter, cerebrospinal fluid, skull compacta and spongiosa, scalp). For each compartment we assisgn a conductivity value based on literature. Diffusion tensor imaging can be used to calculate anisotropic white matter tensor. Based on the segmentation, we create a mesh and 2 mm source grid using Simbio/Vgrid (the necessary files are included). From here, solutions to the EEG and MEG forward problem are calculated in DUNEuro. We then use fieldtrip to preprocess an SEP/SEF data set and calibrate the skull conductivity. See https://doi.org/10.1016/j.neuroimage.2020.117353 DOI 10.1088/1361-6560/abc5aa for more detailed descriptions of the head model creation and the calibration procedure. The entire process can be done in about 1 day with less than an hour active work per subject. You will need at least 30-35 GB RAM. Intermediate results are saved in a temp_res folder. The pipeline is modular and can be run one step at a time. Resulting files include: final segmentation results (temp_res/MRI/*_mask_final.nii) wm_tensors for the mesh (temp_res/mesh/wm_tensors.mat SEP/SEF averages forward_results/*EG_avg.mat) sensors in voxel coordinates plus transformation to ctf space (forward_results/sensors.mat) calibration results (temp_res/calibration/calibration_results.mat) final EEG/MEG lead field (forward_results/LF_*EG.mat + LF_EEG_uncalib.mat) volume conductor information including mesh and source space (forward_results/volume_conductor.mat) From here you can export the lead field and volume conductor to other software packages to solve the inverse problem (interfaces are not provided in the pipeline). All coordinates (sensors, mesh and source space!) are in mm and in voxel space. Apply the transformation to ctf space (the 4x4 matrix in sensors.mat) to switch from voxels to ctf. Also apply the transformation to the lead field. You will need the following software packages: (More recent software versions might work, but were not tested) - Ubuntu 20.04 ( You can use 22.04 but DUNEuro does not compile with gcc >=10: Install e.g. gcc 9.4 and set an according compiler flag in the .opts file when compiling DUNEuro ) - Matlab (2022b) - fieldtrip version (we used an old version from ~May 2020) - fsl version 6 https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation - spm12 including cat12 (segmentation) and the acid toolbox (for white matter anisotropy only) https://neuro-jena.github.io/cat/ http://diffusiontools.com/ - DUNEuro see https://doi.org/10.1371/journal.pone.0252431 for a detailed installation description, a more recent clone script can be found in the pipeline (clone_duneuro.sh and config_release.opts) - install Intel Threading Building Blocks for parallelization (speeds up transfer matrix computation) BEFORE compiling DUNEuro https://www.intel.com/content/www/us/en/docs/onetbb/get-started-guide/2021-6/install-onetbb-on-linux-os.html Update1 : tDCS Create an optimized 8-channel transcranial direct current stimulation cap for a pre-specified dipolar target. uses duneuro-python bindings and the cvx toolbox for D-CMI optimization, see https://doi.org/10.1016/j.brs.2022.12.006 cvx can be downloaded from https://github.com/cvxr/CVX/releases Update 2: CutFEM Contains an exemplary script (CutFEM_EEG.m) that creates level sets and computes an EEG CutFEM lead field. Requires TPMC (https://doi.org/10.1145/3104989), dune-udg. Uses the results from the six-compartment hexahedral mesh pipeline above. There may be a slight mismatch in the source grid, meaning that not all gray matter nodes from the HexFEM source space are inside gray matter cut cells. The CutFEM lead field is only computed for those inside, a dip_outside_gm.mat file is saved that indicates points outside the gray matter. We acknowledge financial support through projectsPerEpi, ERAPERMED2020-227 (TE, MH, CE, SP, CHW),DFG WO1425/10-1, GR2024/8-1, LE1122/7-1 (TE, MH, JOR, CHW),DFG WO1425/7-1 (MA, SS, FN, CHW),ChildBrain, MSCA ITN64165 (MA, MCP, CE, CHW),NIH (Grant Number: 1 R01 EB 026299-01) (TM, CHW),DAAD PPP Finland 57523877 and 57663920(TE, MH, SP, CHW),DFG WO1425/5-1,5-2 (AK, MA, SW, JOR, CHW),DFG WO1425/3-1 (SW, CHW) andDFG WO1425/2-1 (ÜA, JV, CHW).For their collaboration for funding acquisition of the above projects, we thankFabrice Wallois and Alena Buyx (ERAPERMED2020-227),Joachim Gross (DFG GR2024/8-1), Rebekka Lencer (DFG LE1122/7-1),Richard Leahy and John Mosher (1 R01 EB 026299-01),Christoph Herrmann (DFG HE 3353/8-1,8-2) and Till Schneider (DFG SCHN 1511/1-2) andStefan Rampp (DFG RA20662/1-1).