Abstract
It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram (EEG) signals, it was hard to classify the EEG signals and to predict customers' decisions by using traditional classification methods. To solve the aforementioned problems, a recurrent t-distributed stochastic neighbor embedding (t-SNE) neural network was proposed in current study to classify the EEG signals in the designed brand extension paradigm and to predict the participants' decisions (whether to accept the brand extension or not). The recurrent t-SNE neural network contained two steps. In the first step, t-SNE algorithm was performed to extract features from EEG signals. Second, a recurrent neural network with long short-term memory (LSTM) layer, fully connected layer, and SoftMax layer was established to train the features, classify the EEG signals, as well as predict the cognitive performance. The proposed network could give a good prediction with accuracy around 87%. Its superior in prediction accuracy as compared to a recurrent principal component analysis (PCA) network, a recurrent independent component correlation algorithm [independent component analysis (ICA)] network, a t-SNE support vector machine (SVM) network, a t-SNE back propagation (BP) neural network, a deep LSTM neural network, and a convolutional neural network were also demonstrated. Moreover, the performance of the proposed network with different activated channels were also investigated and compared. The results showed that the proposed network could make a relatively good prediction with only 16 channels. The proposed network would become a potentially useful tool to help a company in making marketing decisions and to help uncover the neural mechanisms behind individuals' decision-making behavior with low cost and high efficiency.
Highlights
Neuroscience methods such as event-related potentials (ERPs) were widely used to investigate consumers’ underlying thoughts, feelings, and intensions in marketing researches (Hsu, 2017)
We had a t-distributed stochastic neighbor embedding (t-SNE) support vector machine (SVM) network and a t-SNE back propagation (BP) neural network belonging to the second group of methods
PCA is a kind of principal component analysis method, which has been widely used in feature extraction and Method a recurrent t-SNE neural network t-SNE SVM method t-SNE BP neural network
Summary
Neuroscience methods such as event-related potentials (ERPs) were widely used to investigate consumers’ underlying thoughts, feelings, and intensions in marketing researches (Hsu, 2017). Fu et al (2019) investigated the impact of price deception on consumers’ purchase intension by combining behavior and ERPs measures. They concluded an attenuated N2 and an increased late positive potential (LPP) under the truthful condition. GolnarNik et al (2019) explored the impact of the advertisement on the consumers’ shopping behaviors by using ERP methods They performed feature extraction on the EEG spectral power, which had a very good prediction of decision-making incidence but with low preference classification accuracy. Jin et al (2017) investigated the role of physical attractiveness played in online lending using ERP methods They reported smaller N200 amplitude induced by attractive borrowers compared with the unattractive ones. As far as we are concerned, most of the current studies did not delve further into the EEG signals, and the relationship between the components of the signals and the customers’ decision-making behavior was still unclear
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