Deep Learning for EEG-Based Preference Classification in Neuromarketing

Abstract

The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

Recognition of Consumer Preference by Analysis and Classification EEG Signals

“…In this study, we examined the probability that two affective levels, namely, “like” and “dislike,” could be identified employing different feature combinations of EEG indices as well as different approaches of feature extraction and classification algorithms. We chose these EEG indices based on an analysis of neural correlations of the preference that was explained in our previous research (Aldayel et al, 2020 ). For EEG feature extraction, we used DWT and PSD.…”

“…Our review in Aldayel et al ( 2020 ) highlighted the need to use further features and fusion of classifiers to boost the accuracy of the prediction. In this study, we used a publicly available neuromarketing dataset (Yadava et al, 2017 ) that was previously used (Yadava et al, 2017 ) in building a predictive model for consumer product choice from EEG data.…”

“…On the other hand, consumer's emotions can strongly be influenced by many internal and external factors. The detection and recognition of a consumer's emotional state thus reveal true consumer preferences (Aldayel et al, 2020 ). Although several studies have been conducted on EEG-based emotion recognition (Ramadan et al, 2015 ), EEG-based studies for detecting preferences in consumers are in a very early phase.…”

“…This research aims to thoroughly examine the preference detection in neuromarketing using EEG indices. We chose these EEG indices based on an analysis of neural correlations of the preference that was explained in our previous research (Aldayel et al, 2020 ). We employed two approaches for the extraction of EEG features, namely, discrete wavelet transform (DWT) and power spectral density (PSD).…”

“…This section explains the preference indicators based on EEG signals. Based on our literature review (Aldayel et al, 2020 ), we defined the following four EEG indices to measure people's responses to marketing stimuli: the approach-withdrawal (AW) index, valence, choice index, and effort index. Such indices help marketers in realizing the reactions of consumers to products (Cartocci et al, 2017 ; Cherubino, 2018 ).…”