A self-coherence enhancement algorithm and its application to enhancing three-dimensional source estimation from EEGs

Dezhong Yao, Bin He

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

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 languageEnglish (US)
Pages (from-to)1019-1027
Number of pages9
JournalAnnals of Biomedical Engineering
Volume29
Issue number11
DOIs
StatePublished - 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

Fingerprint

Dive into the research topics of 'A self-coherence enhancement algorithm and its application to enhancing three-dimensional source estimation from EEGs'. Together they form a unique fingerprint.

Cite this