# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review
### Preference Classification Algorithms
A review of some experimental neuromarketing articles and comparisons of computational approaches is presented in Table 5.  
Some preference studies involved the use of more than two classification algorithms to discover well-matched classifiers for a definite feature set [12]. [PERSON] et al. [54] measured the user preferences for the aesthetics presented as virtual 3D shapes by using EEG. The researchers used the frequency bands as features to classify EEG into two classes--liked and disliked--by using the KNN and SVM and achieved high classification accuracies of 80% and 75%, respectively. However, these results cannot be considered reliable because the authors used an extremely small dataset of five subjects. In their extended study [55; 56], the authors increased the number of subjects to 16 but better results were not obtained. [PERSON] et al. [44] achieved an accuracy of 68.5% by using the SVM to predict the most and least favored products by combining EEG measures with questionnaire measures. Classifier combinations such as boosting, voting, or stacking can be used to join numerous classifiers, by merging their outputs and/or training them to complement each other and improve their performance [57]. The selection of the classification algorithms in a BCI system is mostly based on both the form of the acquired mental signals and the context in which the application is expected to be used. However, LDA and SVM are the most commonly applied classification algorithms and have been used in more than half of the EEG-based BCI articles. Another categorization of the classifications was based on the survey research [57], which considered the BCI and machine learning literature from 2007 to 2017. The findings of the recently designed classification algorithms were divided into four main categories: adaptive classifiers, matrix \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Method** & **Number of Studies** & **References** \\ \hline \multirow{4}{*}{**Preprocessing**} & Low-pass filter & 3 & [22; 24; 38] \\ \cline{2-3}  & Band-pass filters & 10 & [3; 11; 15; 20; 28; 39; 40; 41; 44; 53] \\ \cline{2-3}  & Down sampling & 3 & [11; 15; 44] \\ \cline{2-3}  & Average & 7 & [3; 15; 27; 38; 40; 54] \\ \hline \multirow{4}{*}{**Feature Extraction Selection**} & Statistical features: median, & & \\  & standard deviation, t-test, & 9 & [3; 11; 24; 28; 35; 37; 38; 40; 41] \\ \cline{2-3}  & z-score transformation & & \\ \cline{2-3}  & Time–frequency analysis & 2 & [15; 54] \\ \cline{2-3}  & Wavelet domain: entropy, & & \\ energy \(\Rightarrow\) using wavelet transform & 2 & [22; 39] \\ \cline{2-3}  & Frequency domain: power & & \\  & spectral density, band power, & & \\  & spectrum power \(\Rightarrow\) & & \\  & using Fourier transform & & \\ \cline{2-3}  & Correlation analysis: mutual & 3 & [18; 27; 39] \\ \hline \multirow{4}{*}{**Regression/Classification**} & Univariate linear regression analysis & 2 & [3; 24] \\ \cline{2-3}  & SVM & 4 & [15; 22; 54; 55] \\ \cline{2-3}  & KNN & 3 & [15; 54; 55] \\ \cline{2-3}  & DNN & 3 & [22; 23; 55] \\ \cline{2-3}  & RF & 1 & [22; 55] \\ \cline{2-3}  & HMM & 1 & [22] \\ \cline{2-3}  & Linear regression (Lasso, Ridge) & 1 & [27] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Computational approaches for assessing the customer preferences. and tensor classifiers, transfer learning, and deep learning. The adaptive classifiers are classifiers whose parameters, such as the feature weights, are gradually re-assessed and revised over time as new EEG data are presented. The matrix and tensor classifiers (multi-way array) avoid the use of the filters and feature selections and map the data directly onto a certain space with appropriate measures. The transfer learning approach aims to improve the performance of a learned classifier trained on a domain based on the information acquired, while learning another domain or task. In recent times, deep learning has been employed in EEG-based preference recognition.