# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review
### Preference Detection Using a BCI
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Reference** & \begin{tabular}{l} **Classification** \\ **Algorithm** \\ \end{tabular} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Feature** \\ **Channel** \\ \end{tabular} } & \multicolumn{1}{c}{**
\begin{tabular}{c} **Class** \\ **Rhythms** \\ \end{tabular} } & \multicolumn{1}{c}{**Best Accuracy**} \\ \hline
[54] & SVM & F3 and F4 &
\begin{tabular}{l} (Alpha and theta), \\ and (alpha, beta and theta) \\ \end{tabular} & 1.Liked & 75\% \\ \cline{2-6}  & KNN & Fz, F3, F4 & Alpha, theta and delta & 80\% \\ \hline
[49] & SVM & \
\begin{tabular}{l} AF3, F7, F3, FC, T7, \\ PT, O1, O2, P8, T8, \\ PC6, F4, P8, and AF4 \\ \end{tabular} &
\begin{tabular}{l} Alpha, beta, theta, and gamma \\ 2. Unnoticed image \\ \end{tabular} & 75.44\% \\ \cline{2-6}  & F8 and T8 & Alpha & 83.64\% \\ \hline
[15,51] & KNN + TF & \
\begin{tabular}{l} AF3, F7, F3, FC, T7, \\ PT, O1, O2, P8, T8, \\ PC6, F4, P8, and AF4 \\ \end{tabular} &
\begin{tabular}{l} Delta, theta, alpha, beta and gamma \\ 2.Diskled \\ \end{tabular} & 83.34\% \\ \hline
[47] & SVM + Spectral filter & \begin{tabular}{l} P1 and FP2 \\ \end{tabular} & \
\begin{tabular}{l} Delta, theta, alpha, \\ beta and gamma \\ \end{tabular} & 1.Liked & 68.22\% \\ \cline{2-6}  & SVM + CSP &
\begin{tabular}{l} Theta, alpha, beta and gamma \\ \end{tabular} & 2.Diskled & 74.77\% \\ \hline
[52] & QDA + Fourier transform & \begin{tabular}{l} AF3, AF4, F7, F8, 73 \\ F4, FC, PC6, T7, T8, \\ TP, P5, O1, and O2 \\ \end{tabular} & \
\begin{tabular}{l} Theta, alpha, beta and gamma \\ 77, P5, O1, and O2 \\ \end{tabular} &
\begin{tabular}{l} Theta, alpha, beta and gamma \\ 4.Least preferred \\ \end{tabular} & 97.99\% \\ \hline
[23,55,56] & DNN & \begin{tabular}{l} P0z, Er, Cz, C3, C4, \\ P3, F4, P3, and P4 \\ \end{tabular} & \begin{tabular}{l} Delta, theta, alpha, \\ beta and gamma \\ \end{tabular} & 1.Liked & 63.99\% \\ \cline{2-6}  & DNN with dropout and ReLu & \begin{tabular}{l} P0z, Er, Cz, C3, C4, \\ P3, F4, P3, and P4 \\ \end{tabular} & \begin{tabular}{l} Delta, theta, alpha, \\ beta and gamma \\ \end{tabular} & 1.Liked & 60.19\% \\ \cline{2-6}  & DNN with dropout and tanh & \
\begin{tabular}{l} \\ \end{tabular} &
\begin{tabular}{l} Delta, theta and gamma \\ 79.76\% \\ \end{tabular} \\ \hline
[44] & Logistic Regression & \begin{tabular}{l} P7, F1, F2, F2, F3, \\ F4, Cz, and Pr \\ \end{tabular} & \begin{tabular}{l} Delta, theta, alpha, \\ beta and gamma \\ \end{tabular} & \begin{tabular}{l} 1.Most favored \\ 2.Least favored \\ \end{tabular} & 68.50\% \\ \cline{2-6}  & KNN & \
\begin{tabular}{l} P00z, Er, Cz, and Pr \\ \end{tabular} &
\begin{tabular}{l} Delta, theta, alpha, \\ beta and gamma \\ \end{tabular} & 2.Least favored & 59.96\% \\ \hline \multicolumn{6}{l}{ Decision trees with Adaboost M1} \\ \hline
[22] & DNN (2 layers + Sigmoid) & AF3, F7, F3, FC5, T7, \\ PT, O1, O2, P8, T8, \\ RF & PC6, F4, P8, and AF4 & \begin{tabular}{l} Delta, theta, alpha, \\ beta and gamma \\ \end{tabular} & 1.Liked & 62.88\% \\ \cline{2-6}  & HMM & \
\begin{tabular}{l} Delta, theta and alpha \\ beta and gamma \\ \end{tabular} & 2.Diskled & 68.41\% \\ \cline{2-6}  & &
\begin{tabular}{l} P00z, Er, Cz, and Pr \\ \end{tabular} & 70.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification algorithms applied for recognizing the consumer preferences.