Reduced spatially correlated noise influence using subspace source localization method FINES

Lei Ding, Xiaoliang Xu, Bobby Xu, Ying Ni, Bin He

Research output: Contribution to journalConference articlepeer-review

Abstract

We have developed a high resolution subspace approach for EEG source localization within a realistic geometry inhomogeneous head model. The present study aims to reduce the influence caused by spatially correlated noise from background activities using FINES. Computer simulations were conducted on the realistic geometry head volume conductor model and compared with the classic MUSIC algorithm. The FINES approach was also applied to source localization of motor potentials induced by the execution of finger movement in a human subject. The present results suggest that FINES is insensitive to spatially correlated noise, and has enhanced performance as compared with MUSIC.

Original languageEnglish (US)
Pages (from-to)4393-4396
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 VI
StatePublished - 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Keywords

  • Brain array manifold
  • EEG
  • FINES
  • MUSIC
  • Multiple dipole localization
  • Subspace

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