Neuromarketing Solutions based on EEG Signal Analysis using Machine Learning

Marketing campaigns that promote and market various consumer products are a well-known strategy for increasing sales and market awareness. This simply means the profit of a manufacturing unit would increase. "Neuromarketing" refers to the use of unconscious mechanisms to determine customer preferences for decision-making and behavior prediction. In this work, a predictive modeling method is proposed for recognizing product consumer preferences to online (E-commerce) products as “Likes” and “Dislikes”. Volunteers of various ages were exposed to a variety of consumer products, and their EEG signals and product preferences were recorded. Artificial Neural Networks and other classifiers such as Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors, and Support Vector Machine were used to perform product-wise and subject-wise classification using a user-independent testing method. Though, the subject-wise classification results were relatively low with artificial neural networks (ANN) achieving 50.40 percent and k-Nearest Neighbors achieving 60.89 percent. Furthermore, the results of product-wise classification were relatively higher with 81.23 percent using Artificial Neural Networks and 80.38 percent using Support Vector Machine.

@article{Ullah2022,
  title     = {Neuromarketing Solutions based on EEG Signal Analysis using Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {1},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Asad Ullah and Gulsher Baloch and Ahmed Ali and Abdul Baseer Buriro and Junaid Ahmed and Bilal Ahmed and Saba Akhtar},
  doi       = {10.14569/IJACSA.2022.0130137},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130137}
}