A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data.
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 req...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 46; no. 8; pp. 767 - 778 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105550016&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105550016 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2008 vid: 46 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105550016 NLM18427852 2010012644 10.1007/s11517-008-0341-z NLM18427852 105550016 ppf: 767 ppct: 11 formats: fmt: @attributes: type: P tig: atl: A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data. aug: au: Crevecoeur G Hallez H Van Hese P D'Asseler Y Dupré L Van de Walle R Crevecoeur, Guillaume Hallez, Hans Van Hese, Peter D'Asseler, Yves Dupré, Luc Van de Walle, Rik affil: Department of Electrical Energy, Systems and Automation, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Ghent, Belgium sug: subj: Electroencephalography Methods Epilepsy Diagnosis Models, Biological Signal Processing, Computer Assisted Algorithms ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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