Browsing by Author "Ceylan, Burak"

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    An Eeg Based Liking Status Detection Method for Neuromarketing Applications
    (Institute of Electrical and Electronics Engineers Inc., 2020) Ceylan, Burak; Tuzun, Serkan; Akan, Aydın
    In this study, an estimation system based on electroencephalogram (EEG) signals has been developed for use in neuromarketing applications. Determination of the degree of consumer liking a product by processing the biological data (EEG, facial expressions, eye tracking, Galvanic skin response, etc.) recorded while viewing the product images or videos has become an important research topic. In this study, 32-channel EEG signals were recorded from subjects while they watch two different car advertisement videos, and the liking status was determined. After watching the car commercial videos, the subjects were asked to vote on the rating of different images (front view, front console, side view, rear view, rear light, logo and front grill) of the cars. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) method. Several statistical features were extracted from the resulting Intrinsic Mode Functions and the liking status classification was performed. Classification results obtained with Support Vector Machines (SVM) classifiers indicate that the proposed EEG-based liking detection method may be used in neuromarketing studies. © 2020 IEEE.
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    Liking State Estimation Using Time-Frequency Image Representation of EEG Signals
    (IEEE, 2025) Ceylan, Burak; Cekic, Yalcm; Akan, Aydin
    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.