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Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741

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Start date: 2020Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Learning systemsAuthor: search.filters.author.Ceylan, Burak Gor

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    Consumer Preference Estimation Using EEG Signals and Deep Learning, EEG Sinyalleri ve Derin Öğrenme Kullanılarak Tüketici Beğeni Durum Kestirimi
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ceylan, Burak Gor; Cekic, Yalcin; Akan, Aydin I.; Ceylan, Burak Gor, Istanbul University-Cerrahpasa, Istanbul, Turkey; Cekic, Yalcin, Istanbul University-Cerrahpasa, Istanbul, Turkey; Akan, Aydin I., Istanbul University-Cerrahpasa, Istanbul, Turkey
    Emotion estimation is an extremely critical and current research topic for human-computer interaction. In this study, a liking estimation method using electroencephalogram (EEG) signals is proposed to be used in neuromarketing studies. EEG data recorded while participants watch the advertisement videos of two different automobile brands are processed with deep learning techniques to estimate their liking status. After watching the videos, participants were presented with selected image sections from the advertisements (front view, console, side view, rear view, stop lamp, brand logo and front grille) and were asked to rate their liking by scoring from 1 to 5. EEG signals corresponding to these regions were converted into a two dimensional and RGB colored image using the short-time Fourier transform (STFT) method, and liking status was estimated using Deep Learning. The successful results obtained show that the proposed method can be used in neuromarketing studies. © 2024 Elsevier B.V., All rights reserved.
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    Liking State Estimation Using Time-Frequency Image Representation of EEG Signals, EEG Sinyallerinin Zaman-Frekans Imge G sterimleri Kullanilarak Begeni Durum Kestirimi
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ceylan, Burak Gor; Cekic, Yalcin; Akan, Aydin I.; Ceylan, Burak Gor, Biyomedikal Mühendisliǧi, İstanbul Yeni Yüzyıl Üniversitesi, Zeytinburnu, Turkey; Cekic, Yalcin, Mekatronik Mühendisliǧi, Bahçeşehir Üniversitesi, Istanbul, Turkey; Akan, Aydin I., Elektrik ve Elektronik Mühendisliği Bölümü, Izmir Ekonomi Üniversitesi, Izmir, Turkey
    In recent years, there has been a significant increase in research on emotion and preference state estimation. In this study, a preference prediction method is proposed for use in neuromarketing studies by utilizing time-frequency (TF) energy distribution images derived from electroencephalogram (EEG) signals. EEG signals recorded while participants watched commercials from two different automobile brands were evaluated using deep learning techniques to estimate their preference states. After viewing the advertisements, participants were shown selected visual segments from the commercials (e.g., front view, dashboard, etc.) and asked to rate their preferences on a scale from 1 to 5. The EEG signals corresponding to these segments were transformed into two-dimensional RGB-scaled scalogram/spectrogram images using Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT). Using deep learning models, the proposed method achieved maximum classification accuracies of 86.61% and 87.26%, respectively. © 2025 Elsevier B.V., All rights reserved.