Detection of Consumer Preferences Using EEG Signals

Year 2020, Volume: 8 Issue: 4, 289 - 294, 31.12.2020

Burak Ceylan , Serkan Tüzün , Aydın Akan

https://doi.org/10.18100/ijamec.802214

Abstract

In this study, a liking estimation system based on electroencephalogram (EEG) signals is developed for neuromarketing applications. The determination of the degree of appreciation of a product by consumers has become an important research topic using machine learning methods. Biological data is recorded while viewing product pictures or videos, then processed by signal processing methods. In this study, 32 channel EEG signals are recorded from subjects who watched two different car advertisement videos and the liking status is determined. After watching the advertisement videos, the participants were asked to vote for the rating of the different images (front view, dashboard, side view, rear view, taillight, logo and grille) of the products. The signals corresponding to these different video regions from the EEG recordings were segmented and analyzed by the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD). The statistical features were extracted from Intrinsic Mode Functions (IMF) and the liking status classifications were performed. The classification performance of EMD- and EEMD-based methods are 93.4% and 97.8% respectively on Brand1, and 93.5% and 97.4% respectively on Brand2. In addition, the classification accuracy on both brands combined are 85.1% and 85.7% respectively. The promising results obtained using Support Vector Machines (SVM) show that the proposed EEG-based method may be used in neuromarketing studies.

Keywords

EEG, EEMD, Empirical mode decomposition, Liking status detection, Neuromarketing

Supporting Institution

İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ

Project Number

2017-ONAP-MUMF-0002 and 2019-ONAP-MUMF-0001

Thanks

This study was partially supported by Izmir Katip Celebi University, Scientific Research Projects Coordination Unit, and Project numbers: 2017-ONAP-MUMF-0002 and 2019-ONAP-MUMF-0001

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