Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods
dc.authorscopusid | 57904980500 | |
dc.authorscopusid | 56329345400 | |
dc.authorscopusid | 57904169000 | |
dc.authorscopusid | 57190280446 | |
dc.contributor.author | Koksal, M.Y. | |
dc.contributor.author | Cakar, T. | |
dc.contributor.author | Tuna, E. | |
dc.contributor.author | Girisken, Y. | |
dc.date.accessioned | 2024-07-18T20:17:11Z | |
dc.date.available | 2024-07-18T20:17:11Z | |
dc.date.issued | 2022 | |
dc.description | 30th Signal Processing and Communications Applications Conference, SIU 2022 -- 15 May 2022 through 18 May 2022 -- -- 182415 | en_US |
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. © 2022 IEEE. | en_US |
dc.identifier.doi | 10.1109/SIU55565.2022.9864887 | |
dc.identifier.isbn | 9781665450928 | |
dc.identifier.scopus | 2-s2.0-85138706426 | en_US |
dc.identifier.scopusquality | N/A | en_US |
dc.identifier.uri | https://doi.org/10.1109/SIU55565.2022.9864887 | |
dc.identifier.uri | https://hdl.handle.net/11411/6434 | |
dc.indekslendigikaynak | Scopus | en_US |
dc.language.iso | tr | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | 2022 30th Signal Processing and Communications Applications Conference, SIU 2022 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Decision-Making | en_US |
dc.subject | Feature Extraction | en_US |
dc.subject | Fnırs | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Optical Brain İmaging | en_US |
dc.subject | Behavioral Research | en_US |
dc.subject | Brain Mapping | en_US |
dc.subject | Extraction | en_US |
dc.subject | Feature Extraction | en_US |
dc.subject | Infrared Devices | en_US |
dc.subject | Learning Systems | en_US |
dc.subject | Near İnfrared Spectroscopy | en_US |
dc.subject | Behavioral Patterns | en_US |
dc.subject | Decisions Makings | en_US |
dc.subject | Feature Extraction Methods | en_US |
dc.subject | Features Extraction | en_US |
dc.subject | Fnırs | en_US |
dc.subject | Machine-Learning | en_US |
dc.subject | Optical Brain İmaging | en_US |
dc.subject | Prediction Modelling | en_US |
dc.subject | Decision Making | en_US |
dc.title | Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods | en_US |
dc.title.alternative | fNIRS ve Makine Ö?renmesi ile Be?eni Tahmini: Öznitelik Indirgeme Yöntemlerinin Karşilaştirilmasi | en_US |
dc.type | Conference Object | en_US |