Classification of Products Preference from EEG Signals using SVM Classifier

Alnuman, Nasim; Al-Nasser, Samira; Yasin, Omar

doi:10.1109/icitee49829.2020.9271669

“…This technique is commonly used in the neuromarketing industry (see NMSBA Website), and there is accumulating evidence linking various EEG signals with value-based choice ( Sutton and Davidson, 2000 ; Dmochowski et al, 2012 ; Fuentemilla et al, 2013 ; Khushaba et al, 2013 ; San Martin et al, 2013 ). Several academic studies have already employed EEG recordings to predict subjects’ stated valuation or actual choices ( Vecchiato et al, 2011 ; Kong et al, 2013 ; Ravaja et al, 2013 ; Telpaz et al, 2015 ; Yadava et al, 2017 ; Ramsøy et al, 2018 ; Wei et al, 2018 ; Golnar-nik et al, 2019 ; Kumar et al, 2019 ; Alnuman et al, 2020 ; Pandey et al, 2020 ; Si et al, 2020 ), or population marketing success ( Dmochowski et al, 2014 ; Boksem and Smidts, 2015 ; Venkatraman et al, 2015 ; Barnett and Cerf, 2017 ; Christoforou et al, 2017 ; Guixeres et al, 2017 ; Shestyuk et al, 2019 ; Eijlers et al, 2020 ). However, only a few tried to use the latest computational modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing

Hakim

¹

,

Golan²,

Yefet

³

et al. 2023

Front. Hum. Neurosci.

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There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers’ subjective valuations and predict responses to marketing campaigns. However, the properties of EEG raise difficulties for these aims: small datasets, high dimensionality, elaborate manual feature extraction, intrinsic noise, and between-subject variations. We aimed to overcome these limitations by combining unique techniques of Deep Learning Networks (DLNs), while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DLN to predict subjects’ willingness to pay (WTP) based on their EEG data. In each trial, 213 subjects observed a product’s image, from 72 possible products, and then reported their WTP for the product. The DLN employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 0.276 test root-mean-square-error and 75.09% test accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Network visualizations provided the predictive frequencies of neural activity, their scalp distributions, and critical timepoints, shedding light on the neural mechanisms involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.

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“…This technique is commonly used in the neuromarketing industry (see NMSBA Website), and there is accumulating evidence linking various EEG signals with value-based choice [19]- [23]. Several academic studies have already employed EEG recordings to predict subjects' stated preferences or actual choices [24], [25], [34], [35], [26]- [33], or population marketing success [36]- [43]. However, only a few tried to use the latest computational modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to Pay for Neuromarketing

Hakim¹,

Golan²,

Yefet³

et al. 2021

Preprint

0

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<div> <p>There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers’ preferences and predict responses to marketing campaigns. However, the properties of EEG datasets raise various difficulties when performing predictions on them, such as the small size of data sets, high dimensionality, the need for elaborate feature extraction, intrinsic noise, and unpredictable between-subject variations. We aimed to overcome these limitations by combining unique techniques within a Deep Learning (DL) framework, while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DL model to predict subject-specific preferences based on their EEG data. In each trial, 213 subjects observed a product’s image, out of 72 possible products, and then reported how much they were willing to pay (WTP) for the product. The DL employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 75.09% accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Meanwhile, network visualizations provided the predictive frequencies of neural activity and their scalp distributions, shedding light on the neural mechanism involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.</p> </div> <br>

show abstract

“…This technique is commonly used in the neuromarketing industry (see NMSBA Website), and there is accumulating evidence linking various EEG signals with value-based choice [19]- [23]. Several academic studies have already employed EEG recordings to predict subjects' stated preferences or actual choices [24], [25], [34], [35], [26]- [33], or population marketing success [36]- [43]. However, only a few tried to use the latest computational modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to Pay for Neuromarketing

Hakim¹,

Golan²,

Yefet³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

<div> <p>There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers’ preferences and predict responses to marketing campaigns. However, the properties of EEG datasets raise various difficulties when performing predictions on them, such as the small size of data sets, high dimensionality, the need for elaborate feature extraction, intrinsic noise, and unpredictable between-subject variations. We aimed to overcome these limitations by combining unique techniques within a Deep Learning (DL) framework, while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DL model to predict subject-specific preferences based on their EEG data. In each trial, 213 subjects observed a product’s image, out of 72 possible products, and then reported how much they were willing to pay (WTP) for the product. The DL employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 75.09% accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Meanwhile, network visualizations provided the predictive frequencies of neural activity and their scalp distributions, shedding light on the neural mechanism involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.</p> </div> <br>

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Classification of Products Preference from EEG Signals using SVM Classifier

Cited by 5 publications

References 21 publications

DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing

DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing

DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to Pay for Neuromarketing

DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to Pay for Neuromarketing

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