# Quantitative Classification Model of Composite Product Image Based on Event-Related Potential
## 1 Preface
Electroencephalogram (EEG) technology records the electrical activity generated by the brain's neurons on a person's scalp in real time and provides a method of real-time measurement of cognitive processes because of its high temporal resolution, namely, the so-called \"implicit measurement of cognitive processes\" [9]. Therefore, this method is of great importance in understanding the consumer cognitive process and the formation of emotional preferences. Currently, many researchers have used EEG technology to study consumers' cognitive processes related to product images. [PERSON] et al. [10] used EEG technology to study in depth the neurophysiological responses of consumers when using different products in different e-commerce environments to determine their purchase intentions and preferences for different products. [PERSON]. [PERSON] et al. [11] used functional magnetic resonance imaging (FMRI) to investigate whether scaling and rotating consumers' images while shopping for clothing online stimulated the brain's reward mechanism and pleasure. [PERSON] et al. [12] used various websites as stimulus material. Using ERP experiments, they found that the presence of N1 and P2 components showed that subjects had a good emotional experience with the website, while the LPP (late positive potential) showed that they had a poor emotional experience. The research showed that the emotional effect of different website designs on users could be explored more deeply from the perspective of electrophysiology. [PERSON] [13] et al. used ERP technology to conduct an experimental analysis of wood with different colors, textures and glosses. The results showed that consumers perceived the color and gloss of wood more clearly than texture when viewing wood products, and consumers appreciated dark wood products more in terms of color. [PERSON] et al. [14] studied people's esthetic preferences for ceramic tiles and recorded participants' ERP. They found that the tiles people preferred elicited higher N100 amplitudes, while those they disliked elicited P200 and LPP amplitudes. [PERSON] et al. [15] investigated the effects of the types of images perceived by consumers on their purchase intention and ERP data when buying different types of water products and found that dynamic images of water products stimulated shoppers' positive feelings. The amplitude of N2 decreased, the latency shortened, and the amplitude of LPP increased. [PERSON] et al. [16] investigated the brain waves triggered by cultural and creative products with different degrees of enjoyment, extracted consumers' interest in images of cultural and creative products, and developed an interactive evolutionary design system based on an interactive genetic algorithm (IGA) and a neural network (BPNN). The effectiveness of this method was tested using the design practice of compliant bottles. In this study, the examination of EEG characteristics by consumers is assumed under various conditions of image matching for a specific stimulus displaying the product image during the purchasing process. The objective is to quantify users' subjective image perception and establish a correspondence between EEG signals and the product image space during consumers' image perception. Building upon this correspondence, a model is established to relate brain data points to image matching. The product image serves as a significant factor influencing user experience, and ERP (event-related potential) technology emerges as an effective approach to explore user experience. Therefore, this research employs ERP technology to capture and quantify consumers' psychological perception through neural activity in the process of perceiving product images. The correlation model between ERP data and the semantic value of product images is established using SVM (support vector machine).