title: Estimation of number of independent brain electric sources from the scalp EEGs.
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.
authors.affiliation: Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
authors.forename: Xiaoxiao
authors.initials: X
authors.lastname: Bai
authors.forename: Bin
authors.initials: B
authors.lastname: He
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D012535:Scalp / Q000502:physiology