Data-driven head model individualization from exact electrode positions or photogrammetry of the subject's head surface improves M/EEG source localization accuracy!
pca_surfacemesh_warping.py contains the main function of how to start the inidividualization. It is based on a low-dimensional representation (PCA) of head shape surface meshes trained on a equally segmented and triangulated MRI database of 316 subjects. Warping is done by finding weights for the PCs by minimizing the shape difference between electrodes / scalp proxies and fitted scalp.
It contains the following steps:
- Upload your fiducials (NSA, LPA, RPA) and scalp proxy, i.e. sensor positions or any points of the scalp. Ideally points above the ears.
- Using the fiducials, transform the points to the CTF-coordinate system, since the database on which the PCA was applied, lives in CTF space.
- PCA warping, parameters are: number of PCs used for reconstruction, regularizer type (if meshes are intersecting)
- Transform back from CTF in original input space.
- Save surface meshes as .tri (for OpenMEEG) and as .mat for Matlab/FieldTrip or any other mesh format like .stl, .obj, .ply, ....