Neuromarketing Solutions based on EEG Signal Analysis using Machine Learning

• Published in: International journal of advanced computer science & applications 2022, Vol.13 (1)
• Main Authors: Ullah, Asad, Baloch, Gulsher, Ali, Ahmed, Buriro, Abdul Baseer, Ahmed, Junaid, Ahmed, Bilal, Akhtar, Saba
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Classifiers; Decision making; Decision trees; Electroencephalography; Machine learning; Neural networks; Prediction models; Predictions; Signal analysis; Support vector machines
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2022.0130137

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.