Jump to : Download | Abstract | Keywords | Contact | BibTex reference | EndNote reference |

TAV04

N. J. Trujillo-Barreto, E. Aubert-Vazquez, P. A. Valdes-Sosa. Bayesian model averaging in EEG/MEG imaging. NeuroImage, 21(4):1300-1319, 2004.

Download

Download paper: (link)

Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions for a single data. In this case, each model is defined by the set of assumptions of the inverse method used, as well as by the functional dependence between the data and the Primary Current Density (PCD) inside the brain. The key point is that the Bayesian Theory not only provides for posterior estimates of the parameters of interest (the PCD) for a given model, but also gives the possibility of finding posterior expected utilities unconditional on the models assumed. In the present work, this is achieved by considering a third level of inference that has been systematically omitted by previous Bayesian formulations of the IP. This level is known as Bayesian model averaging (BMA). The new approach is illustrated in the case of considering different anatomical constraints for solving the IP of the EEG in the frequency domain. This methodology allows us to address two of the main problems that affect linear inverse solutions (LIS): (a) the existence of ghost sources and (b) the tendency to underestimate deep activity. Both simulated and real experimental data are used to demonstrate the capabilities of the BMA approach, and some of the results are compared with the solutions obtained using the popular low-resolution electromagnetic tomography (LORETA) and its anatomically constraint version (cLORETA)

Keywords

[ Artifacts ] [ *bayes theorem ] [ Brain/*physiology ] [ Brain mapping ] [ Data collection/statistics & numerical data ] [ Dominance ] [ Cerebral/physiology ] [ Electroencephalography/*statistics & numerical data ] [ Evoked potentials ] [ Auditory/physiology ] [ Human ] [ Image processing ] [ Computer-assisted/*statistics & numerical data ] [ Imaging ] [ Three-dimensional/*statistics & numerical data ] [ Linear models ] [ Magnetic resonance imaging ] [ Magnetoencephalography/*statistics & numerical data ] [ Mathematical computing ] [ Models ] [ Neurological ] [ Nerve net/physiology ] [ Occipital lobe/physiology ] [ Reproducibility of results ] [ *signal processing ] [ Computer-assisted ] [ Thalamus/physiology ]

Contact

N. J. Trujillo-Barreto
E. Aubert-Vazquez
P. A. Valdes-Sosa

BibTex Reference

@article{TAV04,
   Author = {Trujillo-Barreto, N. J. and Aubert-Vazquez, E. and Valdes-Sosa, P. A.},
   Title = {Bayesian model averaging in {EEG}/{MEG} imaging},
   Journal = {NeuroImage},
   Volume = {21},
   Number = {4},
   Pages = {1300--1319},
   Year = {2004}
}

EndNote Reference [help]

Get EndNote Reference (.ref)