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Review
. 2024 Jun 5;11(1):17.
doi: 10.1186/s40708-024-00229-8.

A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques

Md Fazlul Karim Khondakar  1 , Md Hasib Sarowar  1 , Mehdi Hasan Chowdhury  2 , Sumit Majumder  1 , Md Azad Hossain  3 , M Ali Akber Dewan  4 , Quazi Delwar Hossain  5
Affiliations
Review

A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques

Md Fazlul Karim Khondakar et al. Brain Inform. .

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.

Keywords: Brain-computer interface (BCI); Classification; Electroencephalography (EEG); Feature; Machine learning; Neuromarketing; Pre-processing; Research trend.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Typical workflow for Neuromarketing research
Fig. 2
Fig. 2
PRISMA flow chart for selecting publications for the systematic review
Fig. 3
Fig. 3
The research trend in Neuromarketing
Fig. 4
Fig. 4
Stimuli used in Neuromarketing applications
Fig. 5
Fig. 5
Traditional EEG pre-processing pipeline
Fig. 6
Fig. 6
Automated EEG pre-processing pipeline
Fig. 7
Fig. 7
Optimal EEG pre-processing pipeline
See this image and copyright information in PMC

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