Abstract
An algorithm was proposed to enhance the spatial resolution of solutions of the under determined electroencephalography (EEG) inverse problem. The self-coherence enhancement algorithm (SCEA) provides a self-coherence solution, which is a function of self-coherence estimate of the under determined EEG inverse solution. The high order self-coherence function was determined by the blurring level of the actual source distribution.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1019-1027 |
| Number of pages | 9 |
| Journal | Annals of Biomedical Engineering |
| Volume | 29 |
| Issue number | 11 |
| DOIs | |
| State | Published - 2001 |
Bibliographical note
Funding Information:The authors wish to thank Jie Lian and Dr. Dong-sheng Wu for valuable discussions. This work was supported in part by NSF CAREER Award BES-9875344 and a grant from The Whitaker Foundation.
Keywords
- Brain imaging
- EEG source reconstruction
- Functional imaging
- Image enhancement
- Minimum norm solution
- Self-coherence solution