# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 4 Application Results and Discussion
The proposed method was more effective than other methods in both binary-class classification \begin{table}
\begin{tabular}{c c c c c} \hline
**Fold** & **kNN** & **SVM** & **RF** & **DeepBiLSTM** \\ \hline F1 & 64.58\% & 51.89\% & 54.23\% & 69.14\% \\ F2 & 63.25\% & 55.78\% & 51.27\% & 71.42\% \\ F3 & 60.24\% & 56.87\% & 56.41\% & 70.83\% \\ F4 & 64.58\% & 51.23\% & 53.62\% & 69.50\% \\ F5 & 66.47\% & 56.41\% & 51.24\% & 72.48\% \\ F6 & 62.96\% & s53.87\% & 55.68\% & 70.20\% \\ F7 & 64.12\% & 58.97\% & 58.00\% & 70.00\% \\ F8 & 62.24\% & 55.87\% & 55.29\% & 69.60\% \\ F9 & 66.39\% & 55.26\% & 58.74\% & 71.22\% \\ F10 & 69.87\% & 58.79\% & 64.26\% & 74.52\% \\ Mean & 64.47\% & 55.49\% & 58.74\% & 70.89\% \\ Standard Deviation & 2.65\% & 2.58\% & 3.86\% & 1.63\% \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy, mean and standard deviation results of positive-negative emotions. \begin{table}
\begin{tabular}{c c c c c} \hline
**Fold** & **kNN** & **SVM** & **RF** & **DeepBiLSTM** \\ \hline F1 & 66.32\% & 62.35\% & 65.85\% & 89.40\% \\ F2 & 61.47\%  
and multi-class classification. One of the biggest reasons for this situation is that the data distribution in multi-class classification is more distinctive than binary-class classification. In Figure 8, the data distribution of positive-negative emotions are given. As can be understood from Figure 8, positive and negative emotions show intensity in a certain area. The close proximity of the data can make the classification process difficult. Looking at the distribution of data, it is not surprising that the results are not good enough. The fact that the values of positive and negative emotions were close to each other caused the classification process to be not reliable enough. We have previously stated that the SVM algorithm is effective when data distribution is clean. This performance of SVM confirms this conclusion. kNN algorithms are more effective in data containing more noise. The ineffectiveness of the algorithm may be since these data do not contain noise. We have stated that the RF algorithm is also effective in large datasets. Our insufficient number of data may have caused this classifier to be ineffective. However, even in this data distribution, the proposed deep learning method has made a remarkably successful classification. This confirms the success of BiLSTM in sequence classification. The main purpose of distinguishing positive and negative emotions is to determine the performance of the proposed method in binary classification. In addition, the number of data is increased by performing binary classification. In this way, the training of the proposed method is made more robust. The data distribution of the multi-class is given in Figure 9. Looking at the data distribution in Figure 9, it is seen that the distribution between points is more uniform than the binary-class classification. Differences in emotions are less than binary-class classification. The classification results also confirm this situation. The more decisive the distinction between the points, the more successful the classifications. One of the biggest reasons for the improved performance of the SVM classifier is that the distinction between these points is clear. kNN has not improved its performance much compared to binary-class classification. One reason for this may be that the data are not suitable for kNN (they do not contain noise), as in binary-class classification. One of the biggest reasons for the increase in the performance of the RF algorithm is the increase in the number of labels. In this way, the number of classes has increased, which significantly affects the performance of the algorithm. Figure 8: Data distribution of positive and negative emotions. (Blue dots signify negative emotions; red dots signify positive emotions). As with every study, this study as certain advantages and disadvantages.