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EEG-Based Preference Classification for Neuromarketing Application

Injamamul Haque Sourov1, Faiyaz Alvi Ahmed1, Md Tawhid Islam Opu1, Aunnoy K. Mutasim2, Mohammad Raihanul Bashar3, Rayhan Sardar Tipu1, Md Ashraful Amin1, Md Kafiul Islam1
1 Affiliation not added yet • 2 University of Calgary • 3 Concordia University
CIN, 2023
DOI
EEG-Based Preference Classification for Neuromarketing Application teaser

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 consumers' preferences based on their EEG data. Several types of analysis were performed using three classifier algorithms: SVM, KNN, and neural-network pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for female subjects and the KNN classifier. In addition, frontal-region electrodes yielded the best selective-channel performance. Conforming to the obtained results, the KNN classifier is deemed best for preference-classification problems. The newly created dataset and results will help research communities conduct further studies in neuromarketing.

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BibTeX

@article{sourov2023eeg,
  author = {Sourov, Injamamul Haque and Ahmed, Faiyaz Alvi and Opu, Md Tawhid Islam and Mutasim, Aunnoy K. and Bashar, Mohammad Raihanul and Tipu, Rayhan Sardar and Amin, Md Ashraful and Islam, Md Kafiul},
  title = {EEG-Based Preference Classification for Neuromarketing Application},
  journal = {Computational Intelligence and Neuroscience},
  volume = {2023},
  pages = {4994751},
  year = {2023},
  doi = {10.1155/2023/4994751}
}