Journal Article2023Cited by 16

EEG‐Based Preference Classification for Neuromarketing Application

Injamamul Haque Sourov, Faiyaz Alvi Ahmed, Md Tawhid Islam Opu, Aunnoy K Mutasim, M Raihanul Bashar, Rayhan Sardar Tipu, Md Ashraful Amin, Md Kafiul Islam

Computational Intelligence and Neuroscience

Wiley, Vol. 2023, Issue 1, ISBN: 1687-5265

DOI: 10.1155/2023/4994751Open AccessURL

Abstract

Neuromarketing is a modern marketing research technique whereby consumers’ behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers’ responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers’ preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing.

Keywords

  • Neuromarketing
  • Support vector machine
  • frontal electrodes
  • EEG
  • k-nearest neighbors
  • EEG dataset
  • artificial neural networks
  • consumer behavior

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

Md Kafiul Islam, PhD
Md Kafiul Islam, PhD

Md Kafiul Islam, PhD

  • Director, BISPL Wing

Professor

Dept. of Electrical and Electronic Engineering

Independent University, Bangladesh (IUB)

Published at

ICPR, IEEE Access

References

  1. 1.Daniel Kahneman, Jackson Beatty. (1966). Pupil Diameter and Load on Memory. Science, 154(3756), 1583–1585[10.1126/science.154.3756.1583]
  2. 2.Samuel M. McClure, Jian Li, Damon Tomlin, Kim S. Cypert, Latané M. Montague, P.Read Montague. (2004). Neural Correlates of Behavioral Preference for Culturally Familiar Drinks. Neuron, 44(2), 379–387[10.1016/j.neuron.2004.09.019]
  3. 3.Bruno Laeng, Sylvain Sirois, Gustaf Gredebäck. (2012). Pupillometry. Perspectives on Psychological Science, 7(1), 18–27[10.1177/1745691611427305]
  4. 4.Susanne Erk, Manfred Spitzer, Arthur P. Wunderlich, Lars Galley, Henrik Walter. (2002). Cultural objects modulate reward circuitry. Neuroreport, 13(18), 2499–2503[10.1097/00001756-200212200-00024]
  5. 5.Mahendra Yadava, Pradeep Kumar, Rajkumar Saini, Partha Pratim Roy, Debi Prosad Dogra. (2017). Analysis of EEG signals and its application to neuromarketing. Multimedia Tools and Applications, 76(18), 19087–19111[10.1007/s11042-017-4580-6]
  6. 6.Mashael Aldayel, Mourad Ykhlef, Abeer Al-Nafjan. (2020). Deep Learning for EEG-Based Preference Classification in Neuromarketing. Applied Sciences, 10(4), 1525[10.3390/app10041525]
  7. 7.John R. Rossiter, Richard B. Silberstein, Philip G. Harris, Geoff Nield. (2001). Brain-Imaging Detection of Visual Scene Encoding in Long-term Memory for TV Commercials. Journal of Advertising Research, 41(2), 1–9[10.2501/jar-41-2-13-21]
  8. 8.Lin Hou Chew, Jason Teo, James Mountstephens. (2015). Aesthetic preference recognition of 3D shapes using EEG. Cognitive Neurodynamics, 10(2), 165–173[10.1007/s11571-015-9363-z]
  9. 9.Tim Ambler, Sven Braeutigam, John Stins, Steven Rose, Stephen Swithenby. (2004). Salience and choice: Neural correlates of shopping decisions. Psychology and Marketing, 21(4), 247–261[10.1002/mar.20004]
  10. 10.Michael L. Rothschild, Yong J. Hyun. (1990). Predicting Memory for Components of TV Commercials from EEG. Journal of Consumer Research, 16(4), 472[10.1086/209232]
  11. 11.M. Murugappan, Subbulakshmi Murugappan, Balaganapathy, Celestin Gerard. (2014). Wireless EEG signals based Neuromarketing system using Fast Fourier Transform (FFT). , 25–30[10.1109/cspa.2014.6805714]
  12. 12.Douglas L. Fugate. (2008). Marketing services more effectively with neuromarketing research: a look into the future. Journal of Services Marketing, 22(2), 170–173[10.1108/08876040810862903]
  13. 13.Mashael S. Aldayel, Mourad Ykhlef, Abeer N. Al-Nafjan. (2020). Electroencephalogram-Based Preference Prediction Using Deep Transfer Learning. IEEE Access, 8, 176818–176829[10.1109/access.2020.3027429]
  14. 14.Hooi Nee. Oon, A. Saidatul, Z. Ibrahim. (2018). Analysis on Non-Linear Features of Electroencephalogram (EEG) Signal for Neuromarketing Application. , 1–8[10.1109/icassda.2018.8477618]
  15. 15.Jason Teo, Chew Lin Hou, James Mountstephens. (2017). Deep learning for EEG-Based preference classification. AIP conference proceedings, 1891, 020141[10.1063/1.5005474]
  16. 16.Jason Teo, Lin Hou, Jia Tian, James Mountstephens. (2018). Classification of Affective States via EEG and Deep Learning. International Journal of Advanced Computer Science and Applications, 9(5)[10.14569/ijacsa.2018.090517]
  17. 17.Michael L. Rothschild, Yong J. Hyun, Bryon Reeves, Esther Thorson, Robert Goldstein. (1988). Hemispherically Lateralized EEG as a Response to Television Commercials. Journal of Consumer Research, 15(2), 185[10.1086/209156]
  18. 18.MICHAEL L. ROTHSCHILD, ESTHER THORSON, BYRON REEVES, JUDITH E. HIRSCH, ROBERT GOLDSTEIN. (1986). EEG ACTIVITY AND THE PROCESSING OF TELEVISION COMMERCIALS. Communication Research, 13(2), 182–220[10.1177/009365086013002003]
  19. 19.Yaozhang Pan, Cuntai Guan, Juanhong Yu, Kai Keng Ang, Ti Eu Chan. (2013). Common frequency pattern for music preference identification using frontal EEG. , 505–508[10.1109/ner.2013.6695982]
  20. 20.Abdurrahman Özbeyaz. (2021). EEG-Based classification of branded and unbranded stimuli associating with smartphone products: comparison of several machine learning algorithms. Neural Computing and Applications, 33(9), 4579–4593[10.1007/s00521-021-05779-0]
  21. 21.Youngrae Kim, Kyuchang Kang, Hyungjik Lee, Changseok Bae. (2014). Preference Measurement Using User Response Electroencephalogram. Lecture notes in electrical engineering, 1315–1324[10.1007/978-3-662-45402-2_183]
  22. 22.M. Agustina Garcés, Lorena L. Orosco. (2008). EEG signal processing in brain–computer interface. Elsevier eBooks, 95–110[10.1016/b978-0-12-812892-3.00005-4]
  23. 23.Aleksandar Milchevski, Marjan Gusev. (2017). Performance Evaluation of FIR and IIR Filtering of ECG Signals. Advances in intelligent systems and computing, 103–112[10.1007/978-3-319-68855-8_10]

Cited By

  1. 1.Md. Fazlul Karim Khondakar, Md. Hasib Sarowar, Mehdi Hasan Chowdhury, Sumit Majumder, Md. Azad Hossain, M. Ali Akber Dewan, Quazi Delwar Hossain. (2024). A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. Brain Informatics, 11(1), 17[10.1186/s40708-024-00229-8]
  2. 2.Mounir Ouzir, Houda Chakir Lamrani, Rachel L. Bradley, Ismail El Moudden. (2023). Neuromarketing and decision-making: Classification of consumer preferences based on changes analysis in the EEG signal of brain regions. Biomedical Signal Processing and Control, 87, 105469[10.1016/j.bspc.2023.105469]
  3. 3.Shumin Li, Niccolò Becattini, Gaetano Cascini. (2024). Neuro-Cognitive Insights Into Engineering Design: Exploring Electroencephalography Predictive Associations With Task Performance. Journal of Mechanical Design, 147(5)[10.1115/1.4066681]
  4. 4.Mokhtar Rabhi, Ahlam Thamri, Elhadja Saida. (2023). Neuromarketing research trends over the last ten years: a bibliometric analysis of the Scopus databases. Marketing and Digital Technologies, 7(3), 7–23[10.15276/mdt.7.3.2023.1]
  5. 5.Hojjat Azadravesh, Reza Sheibani, Yahya Forghani. (2024). Predicted consumer buying behavior in neural marketing based on convolutional neural network and short-term long-term memory. Multimedia Tools and Applications, 84(16), 16835–16851[10.1007/s11042-024-19742-3]
  6. 6.Samira Nazari Ghazvini, Elmira Nazari, Nor Zafir Md Salleh, Rohaizat Baharun. (2024). The Role and Application of Artificial Intelligence in Neuromarketing Research Based on Electroencephalography. International Journal of Academic Research in Business and Social Sciences, 14(12)[10.6007/ijarbss/v14-i12/24111]
  7. 7.Kaining Zhang, Theophilus Teo, Eunhee Chang, Xianglin Zheng, Allison Jing, Mark Billinghurst. (2025). The Brain Knows What You Prefer: Using EEG to Decode AR Input Preferences. CHI Conference on Human Factors in Computing Systems, 1–20[10.1145/3706598.3713896]
  8. 8.Suzan Saban, Eda Dağdevir. (2025). Classification of Consumer Preferences Using EEG Signals: A Machine Learning Approach in Neuromarketing Context. International Scientific and Vocational Studies Journal, 9(2), 210–221[10.47897/bilmes.1821729]
  9. 9.Farhan Ishtiaque, Mohammad Tohidul Islam Miya, Fazla Rabbi Mashrur, Khandoker Mahmudur Rahman, Ravi Vaidyanathan, Syed Ferhat Anwar, Farhana Sarker, Huam Hon Tat, Abu Bakar Abdul Hamid, Khondaker A. Mamun. (2025). Systematic comparison between a research-grade EEG device and a consumer-grade BCI device for predicting consumer preference using an ML framework. Multimedia Tools and Applications, 84(34), 42807–42825[10.1007/s11042-025-20812-3]
  10. 10.Ana Carretero, Alvaro Araujo. (2024). Design Decisions for Wearable EEG to Detect Motor Imagery Movements. Sensors, 24(15), 4763[10.3390/s24154763]
  11. 11.Villa Nanda Permata, Lisda Asdianty, Inung Wijayanto, Sofia Hertiana. (2026). Multi-Region EEG Bandpower Analysis for Smartphone Preference Assessment in Neuromarketing Application. , 1–6[10.1109/aims71270.2026.11712938]
  12. 12.Lisda Asdianty, Villa Nanda Permata, Inung Wijayanto, Sofia Hertiana. (2026). EEG Band Power Analysis of Like and Dislike Responses to Laptop Product Stimuli in Neuromarketing. , 1–6[10.1109/aims71270.2026.11712834]
  13. 13.D. Maithra, J. Udhayakumar. (2026). AI-Driven Insights into Neuromarketing and Social Media Engagement for Enhancing Brand Loyalty. , 1–7[10.1109/ci2a69097.2026.11576915]
  14. 14.Kumar Chandar S., Vijayadurai J., Palanivel Rajan M.. (2025). A Spiking Neural Network Approach to Electroencephalography based Consumer Preference Modeling. Journal of Innovative Image Processing, 7(3), 725–738[10.36548/jiip.2025.3.009]
  15. 15.Olga B. Yarosh, Zinaida A. Zhavoronkova. (2024). Customer perception of souvenir clothing ethnic ornaments: Results of using neuromarketing technologies and artificial intelligence. Upravlenets, 15(3), 57–70[10.29141/2218-5003-2024-15-3-4]
  16. 16.Francisco J. Quevedo. (2024). Humanizing CRM: Appealing to Intuition, the Senses and Emotions to Optimize Customer Relationships. Business, management and economics[10.5772/intechopen.113964]
← All publications