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PubMed · 17073320

Estimating parametric line-source models with electroencephalography.

Nannan Cao·Imam Samil Yetik·Arye Nehorai·Carlos H Muravchik·Jens Haueisen
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Abstract

We develop three parametric models for electroencephalography (EEG) to estimate current sources that are spatially distributed on a line. We assume a realistic head model and solve the EEG forward problem using the boundary element method (BEM). We present the models with increasing degrees of freedom, provide the forward solutions, and derive the maximum-likelihood estimates as well as Cramér-Rao bounds of the unknown source parameters. A series of experiments are conducted to evaluate the applicability of the proposed models. We use numerical examples to demonstrate the usefulness of our line-source models in estimating extended sources. We also apply our models to the real EEG data of N20 response that is known to have an extended source. We observe that the line-source models explain the N20 measurements better than the dipole model.

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Action PotentialsAlgorithmsBrainComputer SimulationDiagnosis, Computer-AssistedElectroencephalographyHumansModels, NeurologicalJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

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Nannan Cao, Imam Samil Yetik, Arye Nehorai, Carlos H Muravchik, Jens Haueisen. 2006. Estimating parametric line-source models with electroencephalography.. https://doi.org/10.1109/tbme.2006.880885

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