# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 6 Conclusions  
This study proposed a DNN model to detect the preferences from the EEG signals by using the pre-processed DEAP dataset. Two types of features were extracted from the EEG: the PSD and valence. This aspect resulted in a group of 2367 unique features illustrating the EEG activity in each trial. We used different evaluations measures (accuracy, recall, and precision) and various validation methods (holdout, LOOCV, and k-fold cross validation) to test classifiers' performance. We built four different classifiers, namely the DNN, RF, SVM, and KNN classifiers, which achieved an accuracy of 94%, 92%, 62%, and 88%, respectively. The results demonstrate that, although the proposed DNN exhibits a higher accuracy, recall, and precision compared with the KNN and SVM, RF reaches similar results to DNN on the same dataset. Future research directions will involve exploring the DNNs in the context of transfer learning for preference detection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cross Validation Method**} & \multirow{2}{*}{**LOOCV**} & \multirow{2}{*}{**Holdout**} & \multicolumn{3}{c}{**k-fold**} \\ \cline{5-6}  & & & **(K = 5)** & **(K = 10)** & **(K = 20)** \\ \hline
**DNN (Binary Cross-entropy)** & 73\% & 93\% & 60\% & 68\% & 71\% \\ \hline
**SVM** & 47\% & 62\% & 49\% & 48\% & 47\% \\ \hline
**RF** & 90\% & 93\% & 86\% & 87\% & 89\% \\ \hline
**KNN** & 92\% & 88\% & 88\% & 90\% & 91\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy results of preference detection with DNN, RF, KNN, and SVM using different cross validation methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**Classifier**} & \multirow{2}{*}{**DNN**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**RF**} & \multicolumn{3}{c}{**KNN**} \\ \cline{5-6}  & & & & **K = 5** & **K = 3** & **K = 1** \\ \hline
**Accuracy** & 94\% & 91\% & 93\% & 62\% & 92\% & 73\% & 79\% & 88\% \\ \hline
**Recall** & 94\% & 91\% & 93\% & 62\% & 92\% & 73\% & 79\% & 88\% \\ \hline
**Precision** & 94\% & 92\% & 94\% & 64\% & 93\% & 75\% & 81\% & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of preference recognition using holdout validation and different classifiers: DNN, SVM, RF, and KNN.  
Figure 7: Loss per epoch on the training and validation sets in DNN using different cross-entropy functions: (**a**) categorical cross-entropy (average loss rate = 0.28); (**b**) Binary cross-entropy (average loss rate = 0.24); and (**c**) hinge cross-entropy (average loss rate = 0.23).  
**Author Contributions:** [PERSON] conceived, designed, and performed the experiment; analyzed and interpreted the data; and drafted the manuscript. [PERSON]-[PERSON] co-supervised the analysis, reviewed the manuscript, and contributed to the discussion. [PERSON] supervised this study. All authors have read and approved the submitted version of the manuscript.  
**Acknowledgments:** The authors would like to thank the deanship of scientific research for funding and supporting this research through the initiative of DSR Graduate Students Research Support (GSR) at King Saud University.  
**Conflicts of Interest:** The authors declare no conflict of interest.