Elektroensefalografi Verilerinin Yarı-güdümlü Öğrenme ile Otomatik Olarak İşaretlenmesi
| dc.contributor.author | Köktürk, Başak Esin | |
| dc.contributor.author | Karaçalı, Bilge | |
| dc.coverage.doi | 10.1109/SIU.2012.6204600 | |
| dc.date.accessioned | 2021-01-24T18:28:52Z | |
| dc.date.available | 2021-01-24T18:28:52Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | In this study, the separation of the stimulus effects from the baseline was investigated in electroencephalography data recorded under different visual stimuli using quasi-supervised learning. The data feature vectors were constructed using independent component analysis and wavelet transform, and then, these feature vectors were separated using quasi-supervised learning. Experiment results showed that the EEG data of the stimuli can be separated using quasi-supervised learning. © 2012 IEEE. | en_US |
| dc.identifier.doi | 10.1109/SIU.2012.6204600 | en_US |
| dc.identifier.isbn | 978-146730056-8 | |
| dc.identifier.scopus | 2-s2.0-84863455167 | |
| dc.identifier.uri | https://doi.org/10.1109/SIU.2012.6204600 | |
| dc.identifier.uri | https://hdl.handle.net/11147/9869 | |
| dc.language.iso | tr | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Electroencephalogram | en_US |
| dc.subject | Independent component analysis | en_US |
| dc.subject | Quasi-supervised learning | en_US |
| dc.subject | Wavelet transform | en_US |
| dc.title | Elektroensefalografi Verilerinin Yarı-güdümlü Öğrenme ile Otomatik Olarak İşaretlenmesi | en_US |
| dc.title.alternative | Automated Labeling of Electroencephalography Data Using Quasi-Supervised Learning | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
| gdc.author.institutional | Köktürk, Başak Esin | |
| gdc.author.institutional | Karaçalı, Bilge | |
| gdc.bip.impulseclass | C5 | |
| gdc.bip.influenceclass | C5 | |
| gdc.bip.popularityclass | C5 | |
| gdc.coar.access | open access | |
| gdc.coar.type | text::conference output | |
| gdc.collaboration.industrial | false | |
| gdc.description.department | İzmir Institute of Technology. Electrical and Electronics Engineering | en_US |
| gdc.description.endpage | 4 | |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.startpage | 1 | |
| gdc.description.wosquality | N/A | |
| gdc.identifier.openalex | W1992476069 | |
| gdc.index.type | Scopus | |
| gdc.oaire.diamondjournal | false | |
| gdc.oaire.impulse | 1.0 | |
| gdc.oaire.influence | 2.7668214E-9 | |
| gdc.oaire.isgreen | false | |
| gdc.oaire.popularity | 6.3377686E-10 | |
| gdc.oaire.publicfunded | false | |
| gdc.oaire.sciencefields | 03 medical and health sciences | |
| gdc.oaire.sciencefields | 0302 clinical medicine | |
| gdc.openalex.collaboration | National | |
| gdc.openalex.fwci | 0.15032766 | |
| gdc.openalex.normalizedpercentile | 0.47 | |
| gdc.opencitations.count | 1 | |
| gdc.plumx.crossrefcites | 1 | |
| gdc.plumx.mendeley | 2 | |
| gdc.plumx.scopuscites | 1 | |
| gdc.scopus.citedcount | 1 | |
| relation.isAuthorOfPublication.latestForDiscovery | a081f8c3-cd7b-40d5-a9ca-74707d1b4dc7 | |
| relation.isOrgUnitOfPublication.latestForDiscovery | 9af2b05f-28ac-4018-8abe-a4dfe192da5e |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Automated_labeling_of_electroencephalography_data_using_quasi-supervised_learning.pdf
- Size:
- 362.37 KB
- Format:
- Adobe Portable Document Format
- Description:
- Article