Classification of Consumer Preferences Using EEG Signals: A Machine Learning Approach in Neuromarketing Context

Yıl 2025, Cilt: 9 Sayı: 2, 210 - 221, 29.12.2025

Suzan Saban , Eda Dağdevir

https://doi.org/10.47897/bilmes.1821729

Öz

In this study, a machine learning approach was developed to classify consumer preferences through EEG signals. Using the open-access Yadava dataset, EEG recordings from 25 male participants aged 18-38 were analysed. Participants were shown different product images, and EEG signals were recorded for each product, along with feedback indicating "like" or "dislike." Data processing was performed using a 50 Hz notch, a 0.5-40 Hz bandpass, and Savitzky-Golay filters to remove noise. The EEG data was then segmented into five brain lobes (frontal, temporal, parietal, occipital, and all lobes) and five frequency bands (delta, theta, alpha, beta, and gamma). In the feature extraction phase, Hjorth parameters (activity, mobility, complexity), entropy-based metrics (Shannon, Tsallis, LogEnergy), statistical features (mean, variance, skewness, kurtosis, etc.), and power spectral density (PSD)-based spectral features were used. The resulting feature matrix was classified using SVM, KNN, NB, and RF algorithms, and 10-fold cross-validation was applied. According to experimental results, the RF algorithm demonstrated the highest performance with 99% accuracy, 100% precision, 99% recall, and 99% F1-score. Furthermore, the KNN algorithm achieved the lowest computational cost in terms of processing time. The study provides a suitable solution for real-time neuromarketing applications because it also considers the computational cost parameter, which is often neglected in real-time system integration. Future studies aim to further enhance emotional state and preference prediction by integrating different classification methods and brain connectivity analyses.

Anahtar Kelimeler

Neuromarketing , EEG , machine learning , signal processing , consumer preferences

Kaynakça

• Ouzir et al., “Neuromarketing and decision-making: Classification of consumer preferences based on changes analysis in the EEG signal of brain regions,” Biomedical Signal Processing and Control, vol. 87, p. 105469, 2024. DOI: 10.1016/j.bspc.2023.105469
• Cherubino et al., “Consumer behaviour through the eyes of neurophysiological measures: State-of-the-art and future trends,” Computational Intelligence and Neuroscience, vol. 2019, p. 1976847, 2019. DOI: 10.1155/2019/1976847
• Khondakar et al., “A systematic review on EEG-based neuromarketing: Recent trends and analyzing techniques,” Brain Informatics, vol. 11, no. 1, p. 17, 2024.
• Zamani & Naieni, “Best feature extraction and classification algorithms for EEG signals in neuromarketing,” Frontiers in Biomedical Technology, 2020.
• Singh et al., “Emotion recognition using electroencephalography (EEG): A review,” International Journal of Information Technology and Knowledge Management, vol. 7, no. 1, 2013.
• Kroupi et al., “Predicting subjective sensation of reality during multimedia consumption based on EEG and peripheral physiological signals,” in 2014 IEEE International Conference on Multimedia Expo (ICME), pp. 1–6, Jul. 2014.
• Golnar-Nik et al., “The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study,” Physiology & Behavior, vol. 207, pp. 90–98, Aug. 2019. DOI: 10.1016/j.physbeh.2019.04.098
• Soria Morillo et al., “Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets,” Biomedical Engineering Online, vol. 15, Jul. 2016. DOI: 10.1186/s12938-016-0181-2
• Chew et al., “Aesthetic preference recognition of 3D shapes using EEG,” Cognitive Neurodynamics, vol. 10, no. 2, pp. 165–173, Apr. 2016. DOI: 10.1007/s11571-015-9363-z
• Baldo et al., “Brain waves predict success of new fashion products: A practical application for the footwear retailing industry,” Journal of Creating Value, vol. 1, no. 1, pp. 61–71, May 2015. DOI: 10.1177/2394964315569625
• Ullah et al., “Neuromarketing solutions based on EEG signal analysis using machine learning,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 1, 2022.
• Sourov et al., “EEG-based preference classification for neuromarketing application,” Computational Intelligence and Neuroscience, vol. 2023, no. 1, Jan. 2023. DOI: 10.1155/2023/4994751
• Zeng et al., “Like/Dislike prediction for sport shoes with electroencephalography: An application of neuromarketing,” Frontiers in Human Neuroscience, vol. 15, Jan. 2022. DOI: 10.3389/fnhum.2021.793952
• Yadava et al., “Analysis of EEG signals and its application to neuromarketing,” Multimedia Tools and Applications, vol. 76, no. 18, pp. 19087–19111, Sep. 2017. DOI: 10.1007/s11042-017-4580-6
• Kumar et al., “Fusion of EEG response and sentiment analysis of product reviews to predict customer satisfaction,” Information Fusion, vol. 52, pp. 41–52, Dec. 2019. DOI: 10.1016/j.inffus.2018.11.001
• Amin et al., “Consumer behavior analysis using EEG signals for neuromarketing application,” in 2020 IEEE Symp. Series Comput. Intell. (SSCI), pp. 2061–2066, Dec. 2020. DOI: 10.1109/SSCI47803.2020.9308304
• M. Aldayel, M. Ykhlef, & A. Al-Nafjan, “Consumers’ Preference Recognition Based on Brain–Computer Interfaces: Advances, Trends, and Applications,” Arab J. Sci. Eng., vol. 46, no. 9, pp. 8983–8997, Sep. 2021. DOI: 10.1007/s13369-021-05695-4.
• M. Aldayel, M. Ykhlef, & A. Al-Nafjan, “Recognition of Consumer Preference by Analysis and Classification EEG Signals,” Front. Hum. Neurosci., vol. 14, Jan. 2021. DOI: 10.3389/fnhum.2020.604639.
• M. Alimardani & M. Kaba, “Deep Learning for Neuromarketing; Classification of User Preference using EEG Signals,” 2021. DOI: 10.1145/3460881.
• D. Panda, D. Das Chakladar, S. Rana, & S. Parayitam, “An EEG-based neuro-recommendation system for improving consumer purchase experience,” J. Consum. Behav., vol. 23, no. 1, pp. 61–75, Jan. 2024. DOI: 10.1002/cb.2142.
• D. Panda, D. Das Chakladar, S. Rana, & M. N. Shamsudin, “Spatial Attention-Enhanced EEG Analysis for Profiling Consumer Choices,” IEEE Access, vol. 12, pp. 13477–13487, 2024. DOI: 10.1109/ACCESS.2024.3355977.
• A. Upadhyay, A. Dubey, P. Goenka, & S. M. Kuriakose, “NMNet: Spatial-Temporal Transformer for EEG Signal Analysis in Neuromarketing,” in ACM Int. Conf. Proceeding Ser., Jan. 2024, pp. 474–478. DOI: 10.1145/3632410.3632472.
• J. G. Proakis & D. G. Manolakis, Digital Signal Processing Principles, Algorithms, and Applications, 1996.
• S. Hargittai, “Savitzky-Golay Least-Squares Polynomial Filters in ECG Signal Processing,” 2005.
• E. Dağdevir and M. Tokmakçi, “Investıgatıon Of The Effect Of Preprocessıng Method On Classıfıcatıon Performance In Motor Imagery Based Braın Computer Interface Systems, M.Sc. thesis, Erciyes University, Turkey.
• N. Sakib, M. K. Islam, & T. Faruk, “Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset,” Comput. Intell. Neurosci., vol. 2023, no. 1, Jan. 2023. DOI: 10.1155/2023/1701429.
• M. Costa, A. L. Goldberger, & C.-K. Peng, “Multiscale entropy analysis of biological signals,” Phys. Rev. E, vol. 71, no. 2, p. 021906, 2005. DOI: 10.1103/PhysRevE.71.021906
• A. Subasi, Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach, Academic Press, 2019.
• C. Tsallis, “The nonadditive entropy Sq and its applications in physics and elsewhere: Some remarks,” MDPI AG, 2011. DOI: 10.3390/e13101765.
• B. Bein, “Entropy,” Mar. 2006. DOI: 10.1016/j.bpa.2005.07.009.
• S. Sanei and J. A. Chambers, EEG Signal Processing. Hoboken, NJ, USA: John Wiley & Sons, 2013.
• D. N. Joanes and C. A. Gill, “Comparing measures of sample skewness and kurtosis,” J. R. Stat. Soc., Ser. D (The Statistician), vol. 47, no. 1, pp. 183–189, 1998. DOI: 10.1111/1467-9884.00122
• R. R. Wilcox, Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy. New York, NY, USA: Springer, 2001.
• E. Niedermeyer and F. H. L. da Silva, Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Philadelphia, PA, USA: Lippincott Williams & Wilkins, 2005.
• P. D. Welch, “The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms,” IEEE Trans. Audio Electroacoust., vol. 15, no. 2, pp. 70–73, 1967. DOI: 10.1109/TAU.1967.1161901
• A. Savitzky and M. J. E. Golay, “Smoothing and differentiation of data by simplified least squares procedures,” Analytical Chemistry, vol. 36, no. 8, pp. 1627–1639, 1964. DOI: 10.1021/ac60214a047
• Lotte et al., “A review of classification algorithms for EEG-based brain–computer interfaces,” J. Neural Eng., vol. 4, no. 2, pp. R1–R13, Jun. 2007. DOI: 10.1088/1741-2560/4/2/R01
• T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
• T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, Jan. 1967. DOI: 10.1109/TIT.1967.1053964
• M. Pal, “Random forest classifier for remote sensing classification,” Int. J. Remote Sens., vol. 26, no. 1, pp. 217–222, Jan. 2005. DOI: 10.1080/01431160412331269698
• M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, Jul. 2009. DOI: 10.1016/j.ipm.2009.03.002
• P. Domingos, “A few useful things to know about machine learning,” Commun. ACM, vol. 55, no. 10, pp. 78–87, 2012. DOI: 10.1145/2347736.2347755