title: Recursive penalized least squares solution for dynamical inverse problems of EEG generation.
abstract: In the dynamical inverse problem of electroencephalogram (EEG) generation where a specific dynamics for the electrical current distribution is assumed, we can impose general spatiotemporal constraints onto the solution by casting the problem into a state space representation and assuming a specific class of parametric models for the dynamics. The Akaike Bayesian Information Criterion (ABIC), which is based on the Type II likelihood, was used to estimate the parameters and evaluate the model. In addition, dynamic low-resolution brain electromagnetic tomography (LORETA), a new approach for estimating the current distribution is introduced. A recursive penalized least squares (RPLS) step forms the main element of our implementation. To obtain improved inverse solutions, dynamic LORETA exploits both spatial and temporal information, whereas LORETA uses only spatial information. A considerable improvement in performance compared to LORETA was found when dynamic LORETA was applied to simulated EEG data, and the new method was applied also to clinical EEG data.
authors.affiliation: Graduate University for Advanced Studies, Tokyo, Japan. yamashi@ism.ac.jp
authors.forename: Okito
authors.initials: O
authors.lastname: Yamashita
authors.forename: Andreas
authors.initials: A
authors.lastname: Galka
authors.forename: Tohru
authors.initials: T
authors.lastname: Ozaki
authors.forename: Rolando
authors.initials: R
authors.lastname: Biscay
authors.forename: Pedro
authors.initials: P
authors.lastname: Valdes-Sosa
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D004569:Electroencephalography; D006801:Humans; D016018:Least-Squares Analysis; D008959:Models, Neurological