title: A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data.
abstract: Epilepsy is a neurological disorder caused by intense electrical activity in the brain. The electrical activity, which can be modelled through the superposition of several electrical dipoles, can be determined in a non-invasive way by analysing the electro-encephalogram. This source localization requires the solution of an inverse problem. Locally convergent optimization algorithms may be trapped in local solutions and when using global optimization techniques, the computational effort can become expensive. Fast recovery of the electrical sources becomes difficult that way. Therefore, there is a need to solve the inverse problem in an accurate and fast way. This paper performs the localization of multiple dipoles using a global-local hybrid algorithm. Global convergence is guaranteed by using space mapping techniques and independent component analysis in a computationally efficient way. The accuracy is locally obtained by using the Recursively Applied and Projected-MUltiple Signal Classification (RAP-MUSIC) algorithm. When using this hybrid algorithm, a four times faster solution is obtained.
authors.affiliation: Department of Electrical Energy, Systems and Automation, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Ghent, Belgium. guillaume.crevecoeur@ugent.be
authors.forename: Guillaume
authors.initials: G
authors.lastname: Crevecoeur
authors.forename: Hans
authors.initials: H
authors.lastname: Hallez
authors.forename: Peter
authors.initials: P
authors.lastname: Van Hese
authors.forename: Yves
authors.initials: Y
authors.lastname: D'Asseler
authors.forename: Luc
authors.initials: L
authors.lastname: Dupré
authors.forename: Rik
authors.initials: R
authors.lastname: Van de Walle
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D008959:Models, Neurological; D012815:Signal Processing, Computer-Assisted