BCI-Based consumers' choice prediction from EEG signals: an intelligent neuromarketing framework
Author(s)
Mashrur, Fazla Rabbi
Rahman, Khandoker Mahmudur
Miya, Mohammad Tohidul Islam
Vaidyanathan, Ravi
Anwar, Syed Ferhat
Type
Journal Article
Abstract
Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.
Date Issued
2022-05-26
Date Acceptance
2022-05-02
Citation
Frontiers in Human Neuroscience, 2022, 16, pp.1-13
ISSN
1662-5161
Publisher
Frontiers Media S.A.
Start Page
1
End Page
13
Journal / Book Title
Frontiers in Human Neuroscience
Volume
16
Copyright Statement
Copyright © 2022 Mashrur, Rahman, Miya, Vaidyanathan, Anwar, Sarker and Mamun. 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.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000808264300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
BRAIN
Brain Computer Interface
CANCER CLASSIFICATION
consumer behavior
consumer neuroscience
electroencephalography
EMG
EMOTION
FEATURES
GENE SELECTION
Life Sciences & Biomedicine
machine learning
neuromarketing
Neurosciences
Neurosciences & Neurology
pattern recognition
PREFRONTAL CORTEX
Psychology
Science & Technology
Social Sciences
SVM-RFE
Publication Status
Published
Article Number
ARTN 861270
Date Publish Online
2022-05-26