Application of Machine Learning in Neuromarketing Research for the Analysis of Customer Preferences

Neuromarketing combines neuroscience and marketing to analyze consumer behavior through tools like electroencephalography (EEG), which captures subconscious and emotional responses. This thesis applies machine learning (ML) techniques to EEG data for predicting purchase decisions, addressing the limitations of traditional marketing methods. Using the NeuMa dataset, which includes EEG and eye-tracking data, key features such as frontal alpha asymmetry (FAA), power spectral density (PSD), and alpha-beta power ratios were extracted to build predictive models. Four ML algorithms—Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)—were evaluated based on accuracy, ROC AUC, and execution time. SVM emerged as the best-performing model, achieving 94.3% accuracy. 99% ROC AUC, with efficient processing time, making it suitable for neuromarketing research. The results confirm the critical role of EEG features from the frontal region, particularly FAA and alpha-beta power ratios, in predicting consumer preferences. These metrics reflect emotional and subconscious responses, emphasizing their importance in purchase decisions. This study demonstrates the value of integrating EEG with ML for consumer analysis, offering a scalable, unbiased, and data-driven approach to marketing research. By combining neuroscience with modern methods, this research provides a foundation for improving consumer preference analysis. It highlights the potential of EEG-based metrics and ML models to enhance marketing strategies, moving beyond traditional self-report methods toward more objective and accurate insights.