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A highly detailed FEM volume conductor model based on the ICBM152 average head template for EEG source imaging and TCS targeting.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Haufe, Stefan Huang, Yu Parra, Lucas C. |
| Copyright Year | 2015 |
| Abstract | In electroencephalographic (EEG) source imaging as well as in transcranial current stimulation (TCS), it is common to model the head using either three-shell boundary element (BEM) or more accurate finite element (FEM) volume conductor models. Since building FEMs is computationally demanding and labor intensive, they are often extensively reused as templates even for subjects with mismatching anatomies. BEMs can in principle be used to efficiently build individual volume conductor models; however, the limiting factor for such individualization are the high acquisition costs of structural magnetic resonance images. Here, we build a highly detailed (0.5mm(3) resolution, 6 tissue type segmentation, 231 electrodes) FEM based on the ICBM152 template, a nonlinear average of 152 adult human heads, which we call ICBM-NY. We show that, through more realistic electrical modeling, our model is similarly accurate as individual BEMs. Moreover, through using an unbiased population average, our model is also more accurate than FEMs built from mismatching individual anatomies. Our model is made available in Matlab format. |
| File Format | PDF HTM / HTML |
| DOI | 10.1109/EMBC.2015.7319697 |
| PubMed reference number | 26737597 |
| Journal | Medline |
| Volume Number | 2015 |
| Alternate Webpage(s) | http://neuralengr.com/public/NYHead/HauHuaPar-embc-2015.pdf |
| Alternate Webpage(s) | http://www.parralab.org/nyhead/HauHuaPar-embc-2015.pdf |
| Alternate Webpage(s) | https://doi.org/10.1109/EMBC.2015.7319697 |
| Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
