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Khondakar MFK, Sarowar MH, Chowdhury MH, Majumder S, Hossain MA, Dewan MAA, Hossain QD. A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. Brain Inform 2024; 11:17. [PMID: 38837089 PMCID: PMC11153447 DOI: 10.1186/s40708-024-00229-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/25/2024] [Indexed: 06/06/2024] Open
Abstract
Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.
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Affiliation(s)
- Md Fazlul Karim Khondakar
- Department of Biomedical Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - Md Hasib Sarowar
- Department of Biomedical Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - Mehdi Hasan Chowdhury
- Department of Electrical & Electronic Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh.
| | - Sumit Majumder
- Department of Biomedical Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - Md Azad Hossain
- Department of Electronics & Telecommunication Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - M Ali Akber Dewan
- School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Athabasca, AB, T9S 3A3, Canada
| | - Quazi Delwar Hossain
- Department of Electrical & Electronic Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
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Panteli A, Kalaitzi E, Fidas CA. A review on the use of eeg for the investigation of the factors that affect Consumer's behavior. Physiol Behav 2024; 278:114509. [PMID: 38485039 DOI: 10.1016/j.physbeh.2024.114509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/20/2024]
Abstract
This literature review surveys research papers that focused on the use of Electroencephalography (EEG) to study the impact of different factors in consumer behavior. The primary aim of this review is to determine which factors that affect consumer's behavior have already been evaluated in the existing literature and which remain unexplored. 118 papers are included in this survey. In order that the papers were analyzed in this review, a well-established neuromarketing experiment should have been performed indicating the methods of signals' acquisition, processing and analysis. The novelty of this work is that it considers and classifies not only research articles that studied a factor that influences consumers' choices, but also those that studied consumers' decisions as a result of the interactions that take place among the received marketing messages and the individual's internal or external environment. Findings indicated that the current approaches have mostly evaluated the effects of the promotional campaigns and product features to consumer's behavior. Also, it was shown that the effect of the interactions among different aspects that influence consumer behavior has not yet adequately been studied.
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Affiliation(s)
- Antiopi Panteli
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece.
| | - Eirini Kalaitzi
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece
| | - Christos A Fidas
- Department of Electrical and Computer Engineering, University of Patras, Patras, 26504, Greece
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Horr NK, Mousavi B, Han K, Li A, Tang R. Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters. Front Neurosci 2023; 17:1191213. [PMID: 38027474 PMCID: PMC10667477 DOI: 10.3389/fnins.2023.1191213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
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.
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Maiseli B, Abdalla AT, Massawe LV, Mbise M, Mkocha K, Nassor NA, Ismail M, Michael J, Kimambo S. Brain-computer interface: trend, challenges, and threats. Brain Inform 2023; 10:20. [PMID: 37540385 PMCID: PMC10403483 DOI: 10.1186/s40708-023-00199-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 07/01/2023] [Indexed: 08/05/2023] Open
Abstract
Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.
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Affiliation(s)
- Baraka Maiseli
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania.
| | - Abdi T Abdalla
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Libe V Massawe
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Mercy Mbise
- Department of Computer Science and Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Khadija Mkocha
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Nassor Ally Nassor
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Moses Ismail
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - James Michael
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Samwel Kimambo
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
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Ntoumanis I, Davydova A, Sheronova J, Panidi K, Kosonogov V, Shestakova AN, Jääskeläinen IP, Klucharev V. Neural mechanisms of expert persuasion on willingness to pay for sugar. Front Behav Neurosci 2023; 17:1147140. [PMID: 36992860 PMCID: PMC10040640 DOI: 10.3389/fnbeh.2023.1147140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 02/20/2023] [Indexed: 03/15/2023] Open
Abstract
Introduction: Sugar consumption is associated with many negative health consequences. It is, therefore, important to understand what can effectively influence individuals to consume less sugar. We recently showed that a healthy eating call by a health expert can significantly decrease the willingness to pay (WTP) for sugar-containing food. Here, we investigate which aspects of neural responses to the same healthy eating call can predict the efficacy of expert persuasion.Methods: Forty-five healthy participants performed two blocks of a bidding task, in which they had to bid on sugar-containing, sugar-free and non-edible products, while their electroencephalography (EEG) was recorded. In between the two blocks, they listened to a healthy eating call by a nutritionist emphasizing the risks of sugar consumption.Results: We found that after listening to the healthy eating call, participants significantly decreased their WTP for sugar-containing products. Moreover, a higher intersubject correlation of EEG (a measure of engagement) during listening to the healthy eating call resulted in a larger decrease in WTP for sugar-containing food. Whether or not a participant’s valuation of a product was highly influenced by the healthy eating call could also be predicted by spatiotemporal patterns of EEG responses to the healthy eating call, using a machine learning classification model. Finally, the healthy eating call increased the amplitude of the P300 component of the visual event-related potential in response to sugar-containing food.Disussion: Overall, our results shed light on the neural basis of expert persuasion and demonstrate that EEG is a powerful tool to design and assess health-related advertisements before they are released to the public.
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Affiliation(s)
- Ioannis Ntoumanis
- International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
- *Correspondence: Ioannis Ntoumanis
| | - Alina Davydova
- International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
| | - Julia Sheronova
- International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
| | - Ksenia Panidi
- International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
| | - Vladimir Kosonogov
- International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
| | - Anna N. Shestakova
- International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
| | - Iiro P. Jääskeläinen
- International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Vasily Klucharev
- International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
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Byrne A, Bonfiglio E, Rigby C, Edelstyn N. A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research. Brain Inform 2022; 9:27. [PMCID: PMC9663791 DOI: 10.1186/s40708-022-00175-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
Abstract
Introduction
The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking.
Methods
Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review.
Results
Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour.
Conclusions and implications
FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.
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