Consumer Preference Estimation Using EEG Signals and Deep Learning

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 vid...

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Bibliographic Details
Published in2024 32nd Signal Processing and Communications Applications Conference (SIU) pp. 1 - 4
Main Authors Ceylan, Burak, Cekic, Yalcin, Akan, Aydin
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.05.2024
Subjects
advertisement
Deep learning
EEG
Electroencephalography
emotional state
Estimation
Fourier transforms
Human computer interaction
liking status
Neuromarketing
Signal processing
STFT
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Summary: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.
DOI:10.1109/SIU61531.2024.10601136