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Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters
The present work investigates whether and how decisions in real-world online shopping scenarios can be predicted based on brain activation. Potential customers were asked to search through product pages on e-commerce platforms and decide, which products to buy, while their EEG signal was recorded. M...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667477/ https://www.ncbi.nlm.nih.gov/pubmed/38027474 http://dx.doi.org/10.3389/fnins.2023.1191213 |
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author | Horr, Ninja Katja Mousavi, Bijan Han, Keren Li, Ao Tang, Ruihong |
author_facet | Horr, Ninja Katja Mousavi, Bijan Han, Keren Li, Ao Tang, Ruihong |
author_sort | Horr, Ninja Katja |
collection | PubMed |
description | The present work investigates whether and how decisions in real-world online shopping scenarios can be predicted based on brain activation. Potential customers were asked to search through product pages on e-commerce platforms and decide, which products to buy, while their EEG signal was recorded. Machine learning algorithms were then trained to distinguish between EEG activation when viewing products that are later bought or put into the shopping card as opposed to products that are later discarded. We find that Hjorth parameters extracted from the raw EEG can be used to predict purchase choices to a high level of accuracy. Above-chance predictions based on Hjorth parameters are achieved via different standard machine learning methods with random forest models showing the best performance of above 80% prediction accuracy in both 2-class (bought or put into card vs. not bought) and 3-class (bought vs. put into card vs. not bought) classification. While conventional EEG signal analysis commonly employs frequency domain features such as alpha or theta power and phase, Hjorth parameters use time domain signals, which can be calculated rapidly with little computational cost. Given the presented evidence that Hjorth parameters are suitable for the prediction of complex behaviors, their potential and remaining challenges for implementation in real-time applications are discussed. |
format | Online Article Text |
id | pubmed-10667477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106674772023-01-01 Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters Horr, Ninja Katja Mousavi, Bijan Han, Keren Li, Ao Tang, Ruihong Front Neurosci Neuroscience The present work investigates whether and how decisions in real-world online shopping scenarios can be predicted based on brain activation. Potential customers were asked to search through product pages on e-commerce platforms and decide, which products to buy, while their EEG signal was recorded. Machine learning algorithms were then trained to distinguish between EEG activation when viewing products that are later bought or put into the shopping card as opposed to products that are later discarded. We find that Hjorth parameters extracted from the raw EEG can be used to predict purchase choices to a high level of accuracy. Above-chance predictions based on Hjorth parameters are achieved via different standard machine learning methods with random forest models showing the best performance of above 80% prediction accuracy in both 2-class (bought or put into card vs. not bought) and 3-class (bought vs. put into card vs. not bought) classification. While conventional EEG signal analysis commonly employs frequency domain features such as alpha or theta power and phase, Hjorth parameters use time domain signals, which can be calculated rapidly with little computational cost. Given the presented evidence that Hjorth parameters are suitable for the prediction of complex behaviors, their potential and remaining challenges for implementation in real-time applications are discussed. Frontiers Media S.A. 2023-11-10 /pmc/articles/PMC10667477/ /pubmed/38027474 http://dx.doi.org/10.3389/fnins.2023.1191213 Text en Copyright © 2023 Horr, Mousavi, Han, Li and Tang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Horr, Ninja Katja Mousavi, Bijan Han, Keren Li, Ao Tang, Ruihong Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters |
title | Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters |
title_full | Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters |
title_fullStr | Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters |
title_full_unstemmed | Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters |
title_short | Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters |
title_sort | human behavior in free search online shopping scenarios can be predicted from eeg activation using hjorth parameters |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667477/ https://www.ncbi.nlm.nih.gov/pubmed/38027474 http://dx.doi.org/10.3389/fnins.2023.1191213 |
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