Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods

dc.contributor.author Koksal, Mehmet Yigit
dc.contributor.author Çakar, Tuna
dc.contributor.author Demircioğlu, Esin Tuna
dc.contributor.author Girisken, Yener
dc.date.accessioned 2022-11-09T07:34:03Z
dc.date.available 2022-11-09T07:34:03Z
dc.date.issued 2022
dc.description.abstract The fMRI method, which is generally used to detect behavioral patterns, draws attention with its expensive and impractical features. On the other hand, near infrared spectroscopy (fNIRS) method is less expensive and portable, but it is as effective as fMRI in creating a good prediction model. With this method, a model has been developed that can predict whether people like a stimulus or not, using machine learning various algorithms. A comparison was made between feature extraction methods, which was the main focus while developing the model.
dc.identifier.citation Köksal, M. Y., Çakar, T., Tuna, E., & Girişken, Y. (15-18 May 2022). Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods. In 2022 30th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. https://doi.org/10.1109/SIU55565.2022.9864887
dc.identifier.doi 10.1109/SIU55565.2022.9864887
dc.identifier.isbn 9781665450928
dc.identifier.issn 2165-0608
dc.identifier.scopus 2-s2.0-85138706426
dc.identifier.uri https://hdl.handle.net/20.500.11779/1882
dc.language.iso tr
dc.publisher IEEE
dc.relation.ispartof 30th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2022 -- Safranbolu, TURKEY
dc.relation.ispartofseries Signal Processing and Communications Applications Conference
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Machine Learning
dc.subject Decision-Making
dc.subject Optical Brain Imaging
dc.subject fNIRS
dc.subject Feature Extraction
dc.title Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods
dc.title.alternative Liking prediction using fNIRS and machine learning: Comparison of feature extraction methods
dc.type Conference Object
dspace.entity.type Publication
gdc.author.id Tuna Çakar / 0000-0001-8594-7399
gdc.author.id Esin Tuna / 0000-0001-6585-4195
gdc.author.institutional Çakar, Tuna
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gdc.bip.influenceclass C5
gdc.bip.popularityclass C5
gdc.coar.access metadata only access
gdc.coar.type text::conference output
gdc.description.department Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
gdc.description.endpage 4
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
gdc.description.startpage 1
gdc.description.woscitationindex Conference Proceedings Citation Index - Science
gdc.identifier.openalex W4293863515
gdc.identifier.wos WOS:001307163400226
gdc.index.type WoS
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gdc.oaire.diamondjournal false
gdc.oaire.impulse 0.0
gdc.oaire.influence 2.5942106E-9
gdc.oaire.isgreen false
gdc.oaire.popularity 2.19756E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 03 medical and health sciences
gdc.oaire.sciencefields 0302 clinical medicine
gdc.oaire.sciencefields 05 social sciences
gdc.oaire.sciencefields 0501 psychology and cognitive sciences
gdc.openalex.collaboration National
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gdc.publishedmonth Mayıs
gdc.relation.journal 2022 30th Signal Processing and Communications Applications Conference, SIU 2022
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gdc.virtual.author Çakar, Tuna
gdc.wos.citedcount 0
gdc.wos.publishedmonth Mayıs
gdc.yokperiod YÖK - 2022-23
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