Abstract
Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.
Highlights
Neuromarketing or consumer neuroscience is an emerging disciplinary area that connects the affective and cognitive aspects of customer behavior utilizing neuroimaging tools such as braincomputer interfaces (BCIs)
The proposed deep neural network (DNN) classifier was compared with three traditional classifiers for EEG signals: k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) using power spectral density (PSD) and discrete wavelet transform (DWT) feature extraction methods as well as various preference indices
A DNN model is proposed for detecting subject preferences from EEG signals using the benchmark neuromarketing dataset
Summary
Neuromarketing or consumer neuroscience is an emerging disciplinary area that connects the affective and cognitive aspects of customer behavior utilizing neuroimaging tools such as braincomputer interfaces (BCIs). Alternative BCI applications for healthy humans have been developed, and an increasing number of these re-searches target fields such as neuromarketing (Al-Nafjan et al, 2017a). The customer looks at the products while EEG data are recorded at the same time on the BCI. The customer rates his/her preference on each product using a nine-point subjective ranking scale. The subjective ranks need to be manually labeled as “preferred” or “unpreferred.” the recorded EEG signals go through preprocessing and feature extraction. This system has three fundamental modules: signal preprocessing, feature extraction, and classification modules
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