Imperial College London
Alternate

ProfessorRaviVaidyanathan

Faculty of Engineering, Department of Mechanical Engineering

Professor in Biomechatronics
 
 
 
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Contact

 

+44 (0)20 7594 7020r.vaidyanathan CV

 
 
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Location

 

717City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Rawnaque:2020:10.1186/s40708-020-00109-x,
author = {Rawnaque, FS and Rahman, KM and Anwar, SF and Vaidyanathan, R and Chau, T and Sarker, F and Mamun, KAA},
doi = {10.1186/s40708-020-00109-x},
journal = {Brain Informatics},
title = {Technological advancements and opportunities in Neuromarketing: a systematic review},
url = {http://dx.doi.org/10.1186/s40708-020-00109-x},
volume = {7},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.
AU  - Rawnaque,FS
AU  - Rahman,KM
AU  - Anwar,SF
AU  - Vaidyanathan,R
AU  - Chau,T
AU  - Sarker,F
AU  - Mamun,KAA
DO  - 10.1186/s40708-020-00109-x
PY  - 2020///
SN  - 2198-4018
TI  - Technological advancements and opportunities in Neuromarketing: a systematic review
T2  - Brain Informatics
UR  - http://dx.doi.org/10.1186/s40708-020-00109-x
UR  - https://www.ncbi.nlm.nih.gov/pubmed/32955675
UR  - http://hdl.handle.net/10044/1/83388
VL  - 7
ER  -
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