Quantitative Classification Model of Composite Product Image Based on Event-Related Potential

Abstract

As an important research tool in neuroscience, event-related potential (ERP) technology enables in-depth analysis of the consumer’s product image cognition process and complements and verifies the product image cognition model at the ERP level. It provides an important theoretical basis for systematically capturing product image and improvement of the product image cognitive model. In this work, the correlation between ERP data, product image word pairs and the degree of semantic match with the product is investigated, and a support vector machine algorithm is selected to build a classification model with physiological data (behavioral data + ERP data) as the independent variable and the degree of semantic match with the product image as the dependent variable. By adjusting the model parameters, the final classification accuracy reaches 95.667%, which shows that the model has some reliability and is a viable research method for ERP-based product image researchers in the future.