Gender Differences in EEG Responses to Color and Black-White Images: Implications for Neuromarketing Strategies.

Analyzing the decision of different genders during shopping is an interesting topic in the neuromarketing industry. This study explores the EEG signal acquisition of twenty subjects in response to Color and Black-White (CL/BW) images and analysis both linear and nonlinear features in different brain regions. The Wilcoxon Rank Sum statistical test was utilized to determine the significance of features in identifying discriminative channels and frequency bands. The results show that the activation of different bands and regions are dependent on the subject's preference and the color of stimuli which are evaluated by spectral scalp map and power spectral density analysis on the the regions. Then, random forest, support vector machine, k-nearest neighbors, and linear discriminant analysis classifiers were also employed to identify the most significant active regions of different brain lobes in both human genders. For the female group in the Like task with CL/BW stimuli images, the classification accuracy significantly increased to 96.47% by combining all frequency bands in the random forest algorithm. On the other hand, for the male group, using the gamma frequency band in the k-nearest neighbors algorithm led to a notable improvement in classification accuracy, reaching 95.32% for the Like task with CL/BW stimuli images. The study provides insights into the influence of black-white colors on marketing products and neuromarketing strategies. The research also revealed differences in the time taken for males and females to make Like and Dislike decisions, as well as the decision-making time for Like and Dislike CL/BW images. The female group took approximately 2.5 seconds to choose an image of a product, whilst the male group took 2.5-3 seconds. The study's findings have significant implications for the field of neuromarketing, emphasizing the importance of careful stimulus selection and classifier choice for classification tasks.