# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 4 Application Results and Discussion
However, based on the results of the classification process made according to the RNN structure, it has been seen that the proposed method is the second most effective in CNN-based approaches. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & **AI** & \multirow{2}{*}{**Emotion**} & **Feature** & **Performance** & **Results** \\  & & & **Model** & **Methods** & **Criteria** & **(Avg. )** \\ \hline \multirow{4}{*}{[19]} & MLPNN, & & Statistical features, DWT, & & \\  & SVM, & & dimensional & Hjorth features, Shannon & & \\  & kNN & & & entropy, logarithmic energy & & \\  & & & & entropy, sample entropy, & & \\  & BiLSTM, & & & multi-scale entropy & & \\  & & & & &  
effective method. While 96% accuracy was obtained in the classification process with LSTM, 76.93% accuracy was obtained in the classification process with BiLSTM. With the proposed method, 90.33% accuracy was calculated. Using different feature extraction methods and increasing the data size can increase the result of this classification. However, since one of the aims of the study was to make an effective classification with a small number of features, the classification result obtained was at least as effective as LSTM. As in this study, BiLSTM architecture was used in study [20] and accuracy of 76.93% was obtained in that study. In that study, features were not extracted from the EEG signals. Raw EEG signals are given directly to the BiLSTM architecture, and the data are classified. In addition to these, single-layer BiLSTM architecture was used in that study. In this study, unlike the study [20], the raw signals were first converted into sub-signals with VMD and EMD, and a total of 14 IMFs were obtained. Then, statistical features were obtained from these 14 IMFs and classification was made using these features. Finally, in this study, a three-layer BiLSTM architecture was used, and the network was provided with a deep structure. As a result, the depth structure of the deep learning model used and the process of obtaining the features are different from the study [20]. These improvements have increased the classification result from 76.93% to 90.33%. The limitations of existing studies can be expressed as follows:  
* The concept of emotion is an abstract structure. It varies from person to person. Therefore, it is not possible to reach a definite conclusion no matter what study is done. * In current studies, visual or aural stimuli are generally used separately. Using them separately does not have a sufficient stimulating effect. * In some other studies in the literature, visual and aural stimuli are used at the same time, and videos or music clips are usually shown for this purpose. However, these stimuli are not as effective as computer games. * Traditional EEG devices are generally used in current studies. This device is both difficult and costly to use. The novelties of the study can be expressed as follows:
* In this study, it was observed that emotion analysis based on EEG signals was more effective. Because EEG signals cannot be manipulated by the subjects and real emotions cannot be hidden. * In this study, it was observed that the classification made according to the emotion model (arousal-valence) was more effective than the discrete emotion model (positive-negative). This showed that as the number of classes increases, the classification process is healthier, and the deep learning model learns better. * Raw EEG signals were used in the study and no specific sampling was performed. In this way, the process load was less, and time was saved. * With this study, it has been observed that portable EEG devices are at least as effective as conventional devices. We think that the results of this study will encourage researchers to use portable devices. * In the study, only statistical methods were used for feature extraction and the data size was not very complex. Only maximum, minimum, and average values were used for feature extraction and the size of the data was reduced.