EEG Based Neuromarketing Recommender System for Video Commercials

Abstract

Video commercials are well known and extremely popular because of the innovative progression in technology and competition in the business fields. Therefore, breaking down the effect of these video commercials are essential before the start of marketing and promotions. Mainly this study addresses a strategy that can assess the viability of movie commercials which are basically trailers, utilizing Electroencephalogram (EEG) signals caught from a Brain-Computer Interface (BCI). This helps the business to have a thought regarding the effect of the commercial and the value. The study consists of a recommend system which suggests movie advertisements based on the preferences of the users. The particular video will be evaluated from emotion, attention and enjoyment aspect of the user. Random Forest prediction algorithm with 91.97% accuracy was used for emotion analysis, c-Support Vector Classifier (c-svc) algorithm with 91.70% accuracy was used for attention analysis while using statistical approach Central Limit theorem and Empirical rule was used for enjoyment analysis to isolate particular emotion state. From the initial results received, confirms that the proposed framework is producing promising results. Although this research is currently focused on movie and entertainment industry, and also has the potential to be developed and applied in many other industries.