A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem

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 non...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 1997-05, Vol.44 (5), p.374-385
Main Authors: Baillet, S., Garnero, L.
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Bayesian methods
Biological and medical sciences
Brain Mapping
Brain modeling
Cognition - physiology
Electrodes
Electrodiagnosis. Electric activity recording
Electroencephalography
Image Processing, Computer-Assisted - methods
Image resolution
Inverse problems
Investigative techniques, diagnostic techniques (general aspects)
Magnetic moments
Magnetic Resonance Imaging
Magnetoencephalography
Medical sciences
Models, Neurological
Models, Statistical
Nervous system
Nonlinear Dynamics
Positron emission tomography
Psychology
Spatial resolution
Surface Properties
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Summary: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).
ISSN:0018-9294
1558-2531
DOI:10.1109/10.568913