Estimation of number of independent brain electric sources from the scalp EEGs

Xiaoxiao Bai, Bin He

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

In electromagnetic source analysis, many source localization strategies require the number of sources as an input parameter (e.g., spatio-temporal dipole fitting and the multiple signal classification). In the present study, an information criterion method, in which the penalty functions are selected based on the spatio-temporal source model, has been developed to estimate the number of independent dipole sources from electromagnetic measurements such as the electroencephalogram (EEG). Computer simulations were conducted to evaluate the effects of various parameters on the estimation of the source number. A three-concentric-spheres head model was used to approximate the head volume conductor. Three kinds of typical signal sources, i.e., the damped sinusoid sources, sinusoid sources with one frequency band and sinusoid sources with two separated frequency bands, were used to simulate the oscillation characteristics of brain electric sources. The simulation results suggest that the present method can provide a good estimate of the number of independent dipole sources from the EEG measurements. In addition, the present simulation results suggest that choosing the optimal penalty function can successfully reduce the effect of noise on the estimation of number of independent sources. The present study suggests that the information criterion method may provide a useful means in estimating the number of independent brain electrical sources from EEG/MEG measurements.

Original languageEnglish (US)
Pages (from-to)1883-1892
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume53
Issue number10
DOIs
StatePublished - Oct 2006

Bibliographical note

Funding Information:
Manuscript received July 29, 2006; revised March 10, 2006. This work was supported in part by the National Institutes of Health (NIH) under Grant R01 EB00178, Grant NSF-BES-0411898, Grant NSF-BES-0411480 and in part by the Supercomputing Institute and the Biomedical Engineering Institute of the University of Minnesota. Asterisk indicates corresponding author.

Keywords

  • Brain mapping
  • Information criterion
  • Source localization
  • Spatio-temporal model

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