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dc.contributor.authorAlp, Yaşar Kemalen_US
dc.contributor.authorArıkan, Orhanen_US
dc.contributor.authorKarakaş, S.en_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2016-02-08T12:27:47Z
dc.date.available2016-02-08T12:27:47Z
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/11693/28714
dc.descriptionDate of Conference: 9-11 April 2009en_US
dc.descriptionConference Name: IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009en_US
dc.description.abstractResolving the sources of neural activity is of prime importance in the analysis of Event Related Potentials (ERP). These sources can be modeled as effective dipoles. Identifying the dipole parameters from the measured multichannel data is called the EEG inverse problem. In this work, we propose a new method for the solution of EEG inverse problem. Our method uses Particle Swarm Optimization (PSO) technique for optimally choosing the dipole parameters. Simulations on synthetic data sets show that our method well localizes the dipoles into their actual locations. In the real data sets, since the actual dipole parameters aren't known, the fit error between the measured data and the reconstructed data is minimized. It has been observed that our method reduces this error to the noise level by localizing only a few dipoles in the brain.en_US
dc.language.isoTurkishen_US
dc.source.titleProceedings of the IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2009.5136319en_US
dc.subjectBrain signalsen_US
dc.subjectDipole sourcesen_US
dc.subjectEvent-related potentialsen_US
dc.subjectMeasured dataen_US
dc.subjectMultichannel dataen_US
dc.subjectNeural activityen_US
dc.subjectNoise levelsen_US
dc.subjectParticle swarm optimization techniqueen_US
dc.subjectReal data setsen_US
dc.subjectSynthetic datasetsen_US
dc.subjectElectroencephalographyen_US
dc.subjectInverse problemsen_US
dc.subjectSignal processingen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.titleDipole source reconstruction of brain signals by using particle swarm optimizationen_US
dc.title.alternativeBeyin sinyallerinin dipol kaynaklarının parçacık sürüsü optimizasyonu kullanarak oluşturulmasıen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage9en_US
dc.citation.epage12en_US
dc.identifier.doi10.1109/SIU.2009.5136319en_US
dc.publisherIEEEen_US


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