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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5641
Title: Analysis of EEG signals and its application to neuromarketing
Authors: Yadava M.
Kumar P.
Saini R.
Pratim Roy, Partha
Prosad Dogra D.
Published in: Multimedia Tools and Applications
Abstract: 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. © 2017, Springer Science+Business Media New York.
Citation: Multimedia Tools and Applications (2017), 76(18): 19087-19111
URI: https://doi.org/10.1007/s11042-017-4580-6
http://repository.iitr.ac.in/handle/123456789/5641
Issue Date: 2017
Publisher: Springer New York LLC
Keywords: Choice prediction
Consumer behavior
EEG
Neuromarketing
Neuroscience
ISSN: 13807501
Author Scopus IDs: 57193672890
57212043589
57190288840
56880478500
55290794700
Author Affiliations: Yadava, M., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Kumar, P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Saini, R., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Prosad Dogra, D., School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India
Corresponding Author: Kumar, P.; Department of Computer Science and Engineering, Indian Institute of TechnologyIndia; email: pradeep.iitr7@gmail.com
Appears in Collections:Journal Publications [CS]

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