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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cook, M.J.D. Koles, Z.J. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Alberta Univ., Edmonton, Alta. (Cook, M.J.D.; Koles, Z.J.) |
| Abstract | Solution of the electroencephalogram (EEG) forward problem in a realistic head model is necessary for accurate source analysis. Realistic head models are usually derived from volumetric magnetic resonance images that provide a voxel resolution of about 1 mm3 . The availability of an electrical head model with this resolution would therefore be extremely advantageous. Head models with resolution in the millimeter range that incorporate the anisotropic properties of their elements have been formulated with the finite element method (FEM). However, these FEM models are fraught with complications related to irregular grids and meshes, along with the incumbent segmentation problems. Presented here is a finite volume method (FVM) formulation of the realistic head model in cubic elements that can ameliorate some of these problems, can incorporate tissue anisotropy, and is both physically intuitive and simple to implement |
| Sponsorship | IEEE EMB |
| Starting Page | 4536 |
| Ending Page | 4539 |
| File Size | 615376 |
| Page Count | 4 |
| File Format | |
| ISBN | 1424400325 |
| ISSN | 1557170X |
| DOI | 10.1109/IEMBS.2006.260314 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-08-30 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Anisotropic magnetoresistance Brain modeling Electroencephalography Magnetic heads Electric potential Bioelectric phenomena Conductivity Diffusion tensor imaging Finite volume methods Finite element methods |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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