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.

<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>

<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>

<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>