# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 5 Conclusions  
In this study, emotion analysis was performed using the EEG data of the GamFEMO dataset. In the study, both the discrete emotion model and the dimensional emotion model were classified. The study consisted of four stages: In the first stage, EEG data were obtained from the dataset. Then, EEG signals were transformed into sub-signals with EMD and VMD methods and a total of 14 IMFs were obtained, including nine from EMDs and five from VMDs. Later, feature extraction process was performed, and statistical features were obtained from these 14 IMFs. Statistical features obtained in the study are maximum value, minimum value and average value. After the feature extraction process, the features were classified with both various machine learning algorithms and the designed deep learning algorithm. The performances of the classifiers were measured with the accuracy score and compared. It was observed that the proposed method performed best in both binary-class classification and multi-class classification. SVM and RF yielded the worst accuracy results in binary-class classification. In contrast, kNN was more effective than these two methods and reached the closest accuracy to the proposed deep learning model. The best performance was achieved with the proposed DeepBiLSTM deep learning model and an accuracy of 70.89% was achieved. In the multi-class classification process, there were noticeable increases in the accuracy performance of SVM, RF and recommended DeepBiLSTM models. In the binary-class emotion analysis process, SVM and RF algorithms did not exceed 60% accuracy, yet they achieved 64.76% and 70.49% accuracy, respectively, in the multi-class classification process. While there was no considerable increase in the kNN algorithm, an effective classification process was performed with the proposed DeepBiLSTM model. In the multi-class analysis process, 90.33% accuracy was achieved with the proposed DeepBiLSTM model. In this way, the effect of the proposed deep learning model was demonstrated. The success of this method in this area will be observed in other datasets and the contribution of the model to the literature will be provided in future studies. In future studies, using different types of portable EEG devices, the performance of such devices in this field will be improved. In addition to these, the number of data will be increased by playing different types of computer games and the effect of this type of stimulus will be determined more clearly. Finally, in future studies, an application based on human-computer interaction that can determine emotion analysis in real-time will be implemented.  
Conceptualization: M.B.; methodology: T.B.A., A.A. and M.B.; software, T.B.A. and M.B.; formal analysis: A.A. and M.B.; writing-original draft preparation: A.A. and M.B.; writing-review and editing: T.B.A.; supervision, M.B.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript. This research has received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: All information about the data used in this study are explained in the third section. The authors declare no conflict of interest.