Authors: Dr. A. Sathiya, Dr. P. Jeyanthi
Abstract: Purchase prediction and understanding its nuances are essential aspects of marketing, but standard approaches do not provide any information about subliminal neural processes which lead to actual purchases. In this paper, a novel multimodal system is proposed which combines EEG neuromarketing data and computer vision features related to visual attention to achieve accurate prediction of consumer purchase intent. A dataset comprising 120 participants who viewed 500 e-commerce images is used for extraction of both EEG-based features (frontal asymmetry of alpha activity, theta/beta ratio, and late positive potential) and visual attention features based on computer vision approach (fixations density, saccades dynamics, and pupils size). Hybrid model consisting of two branches – Temporal Convolutional Network for processing EEG signals and Graph Attention Network for mapping visual attention – reaches 88.3% accuracy and an area under curve equal to 0.94 in predicting consumer purchase intent, while unimodal EEG and visual models reach 74.2% and 72.8% respectively.
DOI: https://doi.org/10.5281/zenodo.20411755
