Understanding consumer preferences for movie trailers from EEG using machine learning

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dc.contributor.author Pandey, Pankaj
dc.contributor.author Swarnkar, Raunak
dc.contributor.author Kakaria, Shobhit
dc.contributor.author Miyapuram, Krishna Prasad
dc.date.accessioned 2020-07-31T11:31:05Z
dc.date.available 2020-07-31T11:31:05Z
dc.date.issued 2020-07
dc.identifier.citation Pandey, Pankaj; Swarnkar, Raunak; Kakaria, Shobhit and Miyapuram, Krishna Prasad, "Understanding consumer preferences for movie trailers from EEG using machine learning", arXiv, Cornell University Library, DOI: arXiv:2007.10756, Jul. 2020. en_US
dc.identifier.uri http://arxiv.org/abs/2007.10756
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5591
dc.description.abstract Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such as EEG have been used to better understand consumer behavior that goes beyond self-report measures which can be a more accurate measure to understand how and why consumers prefer choosing one product over another. Previous studies have shown that consumer preferences can be effectively predicted by understanding changes in evoked responses as captured by EEG. However, understanding ordered preference of choices was not studied earlier. In this study, we try to decipher the evoked responses using EEG while participants were presented with naturalistic stimuli i.e. movie trailers. Using Machine Learning tech niques to mine the patterns in EEG signals, we predicted the movie rating with more than above-chance, 72% accuracy. Our research shows that neural correlates can be an effective predictor of consumer choices and can significantly enhance our understanding of consumer behavior.
dc.description.statementofresponsibility by Pankaj Pandey, Raunak Swarnkar, Shobhit Kakaria and Krishna Prasad Miyapuram
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.title Understanding consumer preferences for movie trailers from EEG using machine learning en_US
dc.type Pre-Print en_US
dc.relation.journal arXiv


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