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{Mashrur:2022:10.1016/j.physbeh.2022.113847,
author = {Mashrur, FR and Rahman, KM and Miya, MTI and Vaidyanathan, R and Anwar, SF and Sarker, F and Mamun, KA},
doi = {10.1016/j.physbeh.2022.113847},
journal = {Physiology and Behavior},
pages = {1--9},
title = {An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals},
url = {http://dx.doi.org/10.1016/j.physbeh.2022.113847},
volume = {253},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billionannually on marketing, promotion, and advertisement using traditional marketing research tools. In addition,these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequentlycriticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises toovercome such constraints. In this work, an EEG-based neuromarketing framework is employed for predictingconsumer future choice (affective attitude) while they view E-commerce products. After preprocessing, threetypes of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapperbased Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negativeaffective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67 ± 2.98,98 ± 3.22, and 98.67 ± 3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition,among all the channels, Fz achieves best accuracy 90 ± 7.81, 90.67 ± 9.53, and 92.67 ± 7.03 for 5-fold, 10-fold,and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketingframework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEGbased neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferencesaccurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.
AU  - Mashrur,FR
AU  - Rahman,KM
AU  - Miya,MTI
AU  - Vaidyanathan,R
AU  - Anwar,SF
AU  - Sarker,F
AU  - Mamun,KA
DO  - 10.1016/j.physbeh.2022.113847
EP  - 9
PY  - 2022///
SN  - 0031-9384
SP  - 1
TI  - An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals
T2  - Physiology and Behavior
UR  - http://dx.doi.org/10.1016/j.physbeh.2022.113847
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000877577100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.sciencedirect.com/science/article/pii/S0031938422001536?via%3Dihub
VL  - 253
ER  -
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