Electroencephalography (EEG) - Based Neuromarketing: Predicting Favourable and Unfavourable Consumer Reactions Using ML Techniques

Abstract

Neuromarketing is an emerging field that combines neuroscience with marketing to gain insights into unconscious consumer behaviour. Traditional methods like surveys often fail to capture real-time emotional and cognitive responses. To address this gap, this study employs EEG signal analysis to predict favourable and unfavourable consumer reactions to advertisements and products. The theoretical foundation is based on the dual-process theory, which distinguishes between fast, emotional decision-making (System 1), and slow, rational thinking (System 2). EEG markers such as alpha, beta, and theta bands are used to assess attention, engagement, and decision conflict. EEG data was collected using a single-channel Neurosky Mindwave headset from 14 participants aged 18–22. A total of 80 ads were shown, categorised by product and design type. Subject-dependent and subject-independent analyses were conducted. In the SD study, Naïve Bayes and SVM classifiers achieved a maximum accuracy of 0.62. In the SI analysis, SVM showed strong performance across product and gender-based classification. A deep learning model also produced comparable accuracy. These findings demonstrate the potential of EEG-based neuromarketing to provide deeper insights into consumer behaviour, with possible implications for both commercial and clinical applications.