EEG-Based Classification of Consumer Preferences Using PCA

Abstract

This study explores the neural correlates of consumer preferences for functional foods using EEG signals from 83 participants. Using Principal Component Analysis (PCA) for dimensionality reduction and visualization, we identified distinctive brain wave patterns associated with liked and disliked food products. PCA revealed dominant activity in Delta (0.97) and Theta (0.92) waves for preferred foods, indicating strong sensoriemotional interaction, while disliked foods showed reduced Alpha (0.23) and Beta (0.14) activity, reflecting decreased cognitive processing. Statistical validation (70\% explained variance using PCA, p < 0.05 in permutation tests) confirmed the robustness. The approach demonstrates how integrating PCA can decode consumer behavior, providing useful insights for neuromarketing and product development, such as optimizing sensory attributes or adapting formulations based on neural profiles. Future work could integrate machine learning for predictive modeling.