Predicting Consumer Behavior: A Critical Review of EEG-Based Neuromarketing and the Decision Tree Model
DOI:
https://doi.org/10.15540/nr.12.2.132Keywords:
Neuropsychology, Neuromarketing, Consumer behavior prediction, EEG signals, Decision Tree (DT) modelAbstract
This critical review examines the study by Amin et al. (2020), which proposes a decision tree (DT) model for predicting consumer behavior using electroencephalogram (EEG)-based neuromarketing. The study leverages EEG signals to analyze consumer responses to marketing stimuli, employing advanced data preprocessing, feature extraction, and classification techniques. The DT model demonstrates superior performance in accuracy, sensitivity, and specificity compared to existing methods, achieving a prediction accuracy of 95%. While the study highlights the potential of EEG-based neuromarketing and the interpretability of the DT model, limitations such as sample size constraints, generalizability concerns, and trade-offs between accuracy and interpretability are noted. The review underscores the model's relevance for developing consumer-centric marketing strategies while calling for further research to address its limitations and expand its applicability across diverse populations.
References
Amin, C. R., Hasin, M. F., Leon, T. S., Aurko, A. B., Tamanna, T., Rahman, M. A., & Parvez, M. Z. (2020, December). Consumer behaviour analysis using EEG signals for neuromarketing application. In 2020 IEEE symposium series on computational intelligence (SSCI) (pp. 2061–2066). IEEE. https://doi.org/10.1109/SSCI47803.2020.9308358
Blankertz, B., Dornhege, G., Krauledat, M., Muller, K.-R., Kunzmann, V., Losch, F., & Curio, G. (2006). The Berlin brain-computer interface: EEG-based communication without subject training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 147–152. https://doi.org/10.1109/TNSRE.2006.875557
Heekeren, H. R., Marrett, S., Bandettini, P. A., & Ungerleider, L. G. (2004). A general mechanism for perceptual decision-making in the human brain. Nature, 431(7010), 859–862. https://doi.org/10.1038/nature02966
Luna, J. M., Gennatas, E. D., Ungar, L. H., Eaton, E., Diffenderfer, E. S., Jensen, S. T., Simone, C. B., Friedman, J. H., Solberg, T. D., & Valdes, G. (2019). Building more accurate decision trees with the additive tree. Proceedings of the National Academy of Sciences, 116(40), 19887–19893. https://doi.org/10.1073/pnas.1816748116
Sixth Factor. (n.d.). Neuro marketing sensonomics. Retrieved March 6, 2025, from https://sixthfactor.com/neuro-marketing-sensonomics/
Yadava, M., Kumar, P., Saini, R., Roy, P. P., & Dogra, D. P. (2017). Analysis of EEG signals and its application to neuromarketing. Multimedia Tools and Applications, 76(18), 19087–19111. https://doi.org/10.1007/s11042-017-4580-6
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Gayatri Kapoor Saraya
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
