Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Neural Decoding of Brand Perception and Preferences: Understanding Consumer Behavior Through Fnirs and Machine Learning(Ieee, 2024-05-15) Çakar, Tuna; Girisken, Yener; Tuna, Esin; Filiz, Gozde; Drias, YassineThis research examines the link between consumer brand perceptions and neural activity by employing Functional Near-Infrared Spectroscopy (fNIRS) and machine learning techniques. The study analyzes the neural projections of participants' reactions to brand-associated adjectives, processing data collected from 168 individuals through machine learning algorithms. The findings underscore the significance of the lateral regions of the prefrontal cortex in the decision- making process related to brand perceptions. The aim is to understand how brands are perceived when associated with various adjectives and to develop this understanding through neural patterns using machine learning models. This study demonstrates the potential of integrating neural data with machine learning methods in the field of applied neuroscience.Conference Object Citation - Scopus: 1Liking Prediction Using fNIRS and Machine Learning: Comparison of Feature Extraction Methods(IEEE, 2022-05-15) Koksal, Mehmet Yigit; Çakar, Tuna; Demircioğlu, Esin Tuna; Girisken, Yener; Tuna, EsinThe 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.