Recognition of Consumer Preference by Analysis and Classification EEG Signals

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.

I think I like this: assessing conscious versus subconscious wine taste responses using neuroscientific techniques

“…Based on previous studies on the role of asymmetric frontal cortical activity in emotional and motivational processes, Harmon- Jones and Gable (2018) assert that much research supports the view that greater left than right frontal cortical activity is associated with greater positively or negatively valanced approach motivation. This is further supported by Aldayel et al (2021) where they state that: "Several studies have shown the efficacy and precision of frontal alpha asymmetry as an essential determinant in emotion and neuromarketing research. "…”

Like/Dislike Prediction for Sport Shoes With Electroencephalography: An Application of Neuromarketing

“…In neuromarketing, PSD is one of the most common feature extraction methods ( Khushaba et al, 2012 ; Yılmaz et al, 2014 ; Golnar-Nik et al, 2019 ). The brain asymmetry-based preference indices such as approach-withdrawal (AW) index, valence, choice index, and effort index are also used as features to predict consumer’s preference ( Aldayel et al, 2020 , 2021 ). Besides, EEG features such as DE and Hjorth parameters have generally been used in some EEG-based applications ( Chen et al, 2019 ; Joshi and Ghongade, 2022 ).…”

“…The effort index measures effort and cognitive processing as higher theta activation in the prefrontal cortex. We used the following equation to calculate the effort index ( Aldayel et al, 2021 ).…”

“…Some studies theorize that the PSD of EEG signals can be used to identify the likes and dislikes of products ( Golnar-Nik et al, 2019 ). Additionally, some studies presume that features and parameters of frontal brain asymmetry, such as approach-withdrawal (AW) index, effort index, choice index, valence, can expertly identify product preference of consumers ( Cartocci et al, 2017 ; Ramsøy et al, 2018 ; Aldayel et al, 2020 , 2021 ). Differential entropy (DE) and Hjorth parameters are commonly used EEG features in emotion recognition ( Chen et al, 2019 ; Joshi and Ghongade, 2022 ).…”

Variation Trends of Fractal Dimension in Epileptic EEG Signals

“…At the same time, EEG signals could also be used to detect psychological and physiological diseases [5,7], which played an important role in the advanced knowledge and treatment of major depression [2,3], autism [1], schizophrenia [8], Parkinson's syndrome [9] and epilepsy. In addition, EEG signals can be used to measure emotion recognition [10][11][12] and sleep quality [13], and had important applications in neuromarketing [14], biometrics [15] and brain-computer interface and games [16].…”