Dipole source reconstruction of brain signals by using particle swarm optimization
buir.contributor.author | Arıkan, Orhan | |
buir.contributor.orcid | Arıkan, Orhan|0000-0002-3698-8888 | |
dc.citation.epage | 12 | en_US |
dc.citation.spage | 9 | en_US |
dc.contributor.author | Alp, Yaşar Kemal | en_US |
dc.contributor.author | Arıkan, Orhan | en_US |
dc.contributor.author | Karakaş, S. | en_US |
dc.coverage.spatial | Antalya, Turkey | en_US |
dc.date.accessioned | 2016-02-08T12:27:47Z | |
dc.date.available | 2016-02-08T12:27:47Z | |
dc.date.issued | 2009 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 9-11 April 2009 | en_US |
dc.description | Conference Name: IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009 | en_US |
dc.description.abstract | Resolving 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.description.provenance | Made available in DSpace on 2016-02-08T12:27:47Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2009 | en |
dc.identifier.doi | 10.1109/SIU.2009.5136319 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/28714 | |
dc.language.iso | Turkish | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/SIU.2009.5136319 | en_US |
dc.source.title | Proceedings of the IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009 | en_US |
dc.subject | Brain signals | en_US |
dc.subject | Dipole sources | en_US |
dc.subject | Event-related potentials | en_US |
dc.subject | Measured data | en_US |
dc.subject | Multichannel data | en_US |
dc.subject | Neural activity | en_US |
dc.subject | Noise levels | en_US |
dc.subject | Particle swarm optimization technique | en_US |
dc.subject | Real data sets | en_US |
dc.subject | Synthetic datasets | en_US |
dc.subject | Electroencephalography | en_US |
dc.subject | Inverse problems | en_US |
dc.subject | Signal processing | en_US |
dc.subject | Particle swarm optimization (PSO) | en_US |
dc.title | Dipole source reconstruction of brain signals by using particle swarm optimization | en_US |
dc.title.alternative | Beyin sinyallerinin dipol kaynaklarının parçacık sürüsü optimizasyonu kullanarak oluşturulması | en_US |
dc.type | Conference Paper | en_US |
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