# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 4 Application Results and Discussion
The ROC provides an illustration of the ratio between sensitivity (true positives) and specificity (true negatives). The ROC analysis provides important information about the performance of the diagnostic test as if the vertex of the curve is close to the upper left corner, the sensitivity rate (true positives) is high and the specificity (true negatives) is low [56; 57]. AUC (area under curve) is also a measure of the discriminative strength of the test. If the AUC value is 0.5, it means that the sensitivity is 50% and the specificity is 50%, meaning there is no differential value. Studies indicate that an AUC of less than 0.75 is not clinically useful and an AUC value of more than 0.90 has a very high clinical value [56]. Figure 7 shows the flow chart of the study. As can be seen from Figure 7, four classifiers were used, and the performance of these classifiers was measured by the accuracy score. The classification results obtained using the proposed method and various machine learning algorithms are given in tables. In the first step (data collection), EEG signals of the GAMEEMO data set were obtained. There are 14 channels in total in the data set and each channel contains 38,252 time series. After the data were obtained, EMD and VMD methods were applied to the EEG signals in the second step (data preprocessing). In this way, the size of the data has been reduced and made suitable for classification. A total of 14 IMFs were obtained, nine IMF for EMD, and five IMF for VMD. In the third step (feature extraction) after the preprocessing, feature extraction was done, and only statistical features were used for this. In the feature extraction process, the maximum, minimum and average values were calculated from the obtained 14 IMFs and the feature vector was created. In the fourth step (data classification), features were classified and SVM, KNN, RF, and BiLSTM classification algorithms were used accordingly. The classification process was carried out for both binary-class and multi-class. While only positive-negative emotions were used in the binary classification, the dimensional model was used in the multi-class classification. HAPV, LAPV, HANV,  
Figure 7: Flow chart of the study. and HAPV zones were considered in the multi-class classification process. In the last step (data evaluation), the performance of the classifiers was evaluated and only the accuracy score was used. The accuracy and some statistical results of the binary-class classification process are shown in Table 2. In Table 3, the accuracy values and some statistical information of the multi-class classification process are given. When the results in Table 2 are examined, generally, it can be seen that the classifiers have not performed very well. The best performance was obtained with the proposed deep learning, and the classification between positive and negative emotions was made with an accuracy rate of 70.89%. The closest performance to the proposed deep learning model was achieved with the kNN algorithm. With SVM and RF, the accuracy values remained under 60% for each fold. The reason why the results were not successful is due to the distribution of the data. In Table 3, the accuracy results of four zones belonging to the arousal-valence plane are given. According to the results in Table 3, it can be observed that the multi-class classification process is more successful than the binary-class classification. The accuracy value of SVM and RF algorithms increased in multi-class classification. The performance of the SVM algorithm in multi-class classification has increased to 64.76%. Similarly, the performance of the RF algorithm has increased noticeably, and the average accuracy has increased up to 70.49%. The accuracy performance of the kNN algorithm has also increased, but no significant increase has been observed as in SVM and RF. Furthermore, looking at the accuracy result of the proposed deep learning model, it has been observed that the proposed method produces a much better result compared to other methods.