First, both the ICBM152 and the AAL116 templates were transformed to the same coordinate space. Here, we chose the MNI coordinates as our standard space coordinate reference. As indicated in Sec. 2.2, an affine transformation was performed on the ICBM 152 atlas after the FEM forward calculation, resulting in standard MNI coordinates for each voxel (, , , , where is the total number of voxels). The MNI coordinates of the original AAL 116 template were obtained from a previous study.32 Second, the surfaces of AAL 116 template were extracted using the software ITK,43 shown in Fig. 3(d), and exported them as vertices (e.g., , , , , where is the total number of voxels) and faces (triangles). To efficiently and correctly identify or classify whether the brain voxels were within a certain AAL region, we followed the algorithm called “point-in-polyhedron problem.”44 Specifically, the first operation, the orientation operation, tested whether a point falls to either positive or negative sides of a triangle within surface. The second operation, point classification operation, classified whether a point is on one of the triangle’s edges or in its interior. After the voxel classification, we were able to group or classify all the voxels in an atlas-DOT brain image into 116 anatomical regions. For example, Fig. 3(e) shows all atlas-DOT voxels on the cortical template classified into six different brain regions, represented by different colors.