Consumer Decision Recognition Based on EEG Signals for Neuromarketing Applications

S. Kumar Chandar, J. Vijayadurai and M. Palanivel Rajan
Additional contact information
S. Kumar Chandar: School of Business & Management, CHRIST University, Madurai Kamaraj University, Karnataka, India
J. Vijayadurai: ��Department of Management Studies, Madurai Kamaraj University, Madurai, Tamil Nadu, India
M. Palanivel Rajan: ��Department of Management Studies, Madurai Kamaraj University, Madurai, Tamil Nadu, India

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 06, 1825-1847

Abstract: Neuromarketing is a blooming interdisciplinary field that tries to understand the biology of consumer behavior by combining neuroscience with marketing. This technique can be used to grasp consumers’ hidden choices, intentions and decisions by analyzing their physiological and brain signals. Electroencephalography (EEG) is one of the popular neuroimaging techniques to capture and record the neural activity of the brain. Numerous research projections have been made in this field to achieve better results. Earlier approaches did not prioritize effective EEG signal preprocessing and classification methods. This paper presents a model to recognize consumer preferences by analyzing and classifying EEG signals. In this model, EEG signals are decomposed into many subbands using wavelet transform. An enhanced wavelet thresholding method is proposed to eliminate noise from subbands. Several wavelet features are computed from each subband and then fed as input to classifiers. Finally, three different machine learning classifiers are used to classify the signal between like and dislike. The classifiers are K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). EEG signals from 25 people are collected to verify the developed system’s performance. The effectiveness of the developed method with different classifiers is validated by varying brain lobe features and band features. In comparison to other classifiers like KNN and MLP, the designed system with the SVM classifier performs better and achieves an accuracy of 98.21%. The experimental findings for the developed system suggest that research in this area has the potential to alter and enhance marketing tactics for the benefit of both manufacturers and consumers, ultimately leading to a mutually beneficial outcome.

Keywords: Classification; consumer choice; EEG; machine learning; neuro-marketing (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622025500245
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:24:y:2025:i:06:n:s0219622025500245

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622025500245

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().