# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 4 Application Results and Discussion
The advantages can be listed as follows:  
* It has been observed that the deep learning algorithm is better than certain machine learning algorithms, despite the small number of data. Generally, the number of data is expected to be high in studies conducted with deep learning. * Although the data distribution is not very clear in binary classification, the proposed deep learning model has been observed to be acceptably successful. * When classifying with deep learning, manual feature extraction is generally not performed. In this study, we performed deep learning after feature extraction. By extracting features from signals, we may have caused certain information to be lost. However, despite this, a successful classification was achieved by the proposed deep learning model. * A lot of information cannot be obtained from statistical features. Generally, effective features in signal processing studies are obtained from time-frequency features [54, 55]. Despite this, the deep learning model has produced successful results. The disadvantages of the study can be listed as follows:
* Due to the large sample length of the signals in the data set, we could not process the raw data. We had to perform feature extraction. This may have caused some information to be lost. Processing the raw data can increase the performance of the deep learning algorithm. * The performances of the deep learning model and other machine learning models vary according to the selected feature extraction methods. More information can be obtained by using other feature extraction methods. This can positively affect the performance of the methods. * Although the number of data is small, the proposed method has been successful. However, this does not mean that the model is reliable. To determine the reliability of the model, different data sets should be used, or the information obtained from the data set used should be reproduced. Figure 10 shows the ROC curve for binary classification, with both classes using DeepBiLSTM. According to Figure 10, the accuracy jumped from 70.89% into 78%. Figure 11 shows the ROC curve for multi-classification, with all classes using DeepBiLSTM. According to Figure 11, the accuracy decreased from 90.33% to 89%. Figure 9: Data distribution of emotions in the arousal-valence plane. (Blue dots indicate LANV; red dots refer LAPV; yellow dots indicate HANV; and purple dots show HAPV emotions). In addition, the obtained results were compared with various studies in the literature. The results of this comparison are given in Table 4.  
Looking at the results in Table 4, it was observed that the results obtained in this study were at least as effective as the aforementioned methods. In addition to these, the results of other studies using the GAMEMO dataset and deep learning algorithms in the literature are given in Table 5. In this way, the proposed method has been compared with other deep learning algorithms. Figure 11: The ROC curve for multi-class classification using DeepBiLSTM. (Pink squares represent micro-average ROC curve, while purple squares represent the macro-average ROC curve). Figure 10: The ROC curve for binary classification using DeepBiLSTM. (Pink squares represent micro-average ROC curve, while purple squares represent the macro-average ROC curve). When the results in Table 5 are examined, it is seen that the designed BiLSTM method is at least as effective as the methods in the literature. However, the proposed method was weaker compared to CNN-based approaches. Images are used in the CNN-based classification process and features are obtained from the images. It is not surprising that the results are more effective in CNN-based approaches. One of the most important reasons for this is the low loss of information while processing the image. Furthermore, its built-in convolutional layer reduces the high dimensionality of images without losing its information.