title: DYNAMO: concurrent dynamic multi-model source localization method for EEG and/or MEG.
abstract: This work presents a new dipolar method to estimate the neural sources from separate or combined EEG and MEG data. The novelty lies in the simultaneous estimation and integration of neural sources from different dynamic models with different parameters, leading to a dynamic multi-model solution for the EEG/MEG source localization problem. The first key aspect of this method is defining the source model as a dipolar dynamic system, which allows for the estimation of the probability distribution of the sources within the Bayesian filter estimation framework. A second important aspect is the consideration of several banks of filters that simultaneously estimate and integrate the neural sources of different models. A third relevant aspect is that the final probability estimate is a result of the probabilistic integration of the neural sources of numerous models. Such characteristics lead to a new approach that does not require a prior definition neither of the number of sources or of the underlying temporal dynamics, allowing for the specification of multiple initial prior estimates. The method was validated by three sensor modalities with simulated data designed to impose difficult estimation situations, and with real EEG data recorded in a feedback error-related potential paradigm. On the basis of these evaluations, the method was able to localize the sources with high accuracy.
authors.affiliation: Computer Science and Systems Engineering Department I3A, University of Zaragoza, Spain. antelis@unizar.es
authors.forename: Javier M
authors.initials: JM
authors.lastname: Antelis
authors.forename: Javier
authors.initials: J
authors.lastname: Minguez
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D015225:Magnetoencephalography; D008959:Models, Neurological; D017711:Nonlinear Dynamics