A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem.
IEEE Trans Biomed Eng
; 44(5): 374-85, 1997 May.
Article
en En
| MEDLINE
| ID: mdl-9125822
ABSTRACT
In this paper, we present a new approach to the recovering of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG) imaging. This method consists in introducing spatial and temporal a priori information as a cure to this ill-posed inverse problem. A nonlinear spatial regularization scheme allows the preservation of dipole moment discontinuities between some a priori noncorrelated sources, for instance, when considering dipoles located on both sides of a sulcus. Moreover, we introduce temporal smoothness constraints on dipole magnitude evolution, at time scales smaller than those of cognitive processes. These priors are easily integrated into a Bayesian formalism, yielding a maximum a posteriori (MAP) estimator of brain electrical activity. Results from EEG simulations of our method are presented and compared with those of classical quadratic regularization and a now popular generalized minimum-norm technique called low-resolution electromagnetic tomography (LORETA).
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Mapeo Encefálico
/
Magnetoencefalografía
/
Teorema de Bayes
/
Electroencefalografía
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Biomed Eng
Año:
1997
Tipo del documento:
Article
País de afiliación:
Francia