Analysis of EEG signals and its application to neuromarketing

• Published in: Multimedia tools and applications Vol. 76; no. 18; pp. 19087 - 19111
• Main Authors: Yadava, Mahendra, Kumar, Pradeep, Saini, Rajkumar, Roy, Partha Pratim, Prosad Dogra, Debi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2017; Springer Nature B.V
• Subjects: Brain research; Business; Computer Communication Networks; Computer Science; Consumer behavior; Data Structures and Information Theory; Decision making; Electroencephalography; Electronic commerce; Marketing; Markov chains; Medical imaging; Multimedia Information Systems; Neurosciences; Prediction models; Preferences; Principal components analysis; Questionnaires; Recommender systems; Special Purpose and Application-Based Systems; Neuroscience; Consumer behavior; Neuromarketing; EEG; Choice prediction
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-017-4580-6

Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer’s comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called “Neuromarketing”. This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of “likes” and “dislikes” by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.