# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
###### Abstract
The right side of the plane specifies positive valence, while the left side indicates negative valence. On the arousal plane, emotions are sorted from inactive emotions to active emotions from bottom to top. In Figure 2, a general structure of the dimensional emotion model is given. Figure 1: The 10–20 system. (**A**) front side, (**B**) top side [11; 12]. As can be understood from Figure 2, the arousal-valence plane is divided into four separate zones. While HAPV (High Arousal Positive Valence) emotions are placed in the first zone, there are HANV (High Arousal Negative Valance) emotions in the second zone. On the contrary, there are low arousal feelings in the third and fourth zones. While the third zone consists of LANV (Low Arousal Negative Valance) emotions, the fourth zone consists of LAPV (Low Arousal Positive Valence) emotions. Thanks to this model structure, emotions are expressed not by their names but by their position in the plane. Therefore, this model is expressed as a universal model [16]. For example, the feeling of \"bored\" is now expressed as LANV. Further, in the literature, there are three different types of stimuli (aural, visual, aural/visual stimuli), where stimuli are used to stimulate a person to reach the emotions to be extracted [17]. Aural stimuli are made up of sounds. It usually consists of music, sound clips. Visual stimuli are like pictures or images labeled with different names. As for aural/visual stimuli, which are considered among the best stimuli for extracting emotions, according to most studies [18; 19], include both sounds and visuals. In this study, the emotion recognition process was performed using both the dimensional emotion model and the discrete emotion model. The study consists of four stages. In the first stage, EEG emotion data were obtained from the GAMEEMO dataset. In the second stage, EEG signals were decomposed by EMD (Empirical Mode Decomposition) and VMD (Variational Mode Decomposition) and a total of 14 IMF (Intrinsic Mode Functions) were obtained for each EEG signal. Nine of the IMFs were obtained from EMD and the remaining five from VMD. Then, statistical features were extracted from IMFs and maximum value, minimum value, and average values were taken into consideration for the feature extraction process. In the last stage of the study, the classification was carried out with the designed DeepBiLSTM (Bidirectional Long-Short Term Memory) method and the performance of the classifier was evaluated with the accuracy value. The performance of the DeepBiLSTM method was also compared with kNN (K-Nearest Neighbors), SVM (Support Vector Machines), and RF (Random Forest). While positive and negative emotions were used for the binary-class classification process, the dimensional model was used for the multi-class classification process. In the binary-class classification process, the proposed deep learning model achieved an average accuracy of 70.89%, this rate increased in multi-class classification and was 90.33% on average. The main contributions of the study can be listed as follows:  
Figure 2: General structure of arousal-valence dimension. * To the best of our knowledge, for the first time in this study, data belonging to the GAMEEMO dataset was analyzed with the DeepBiLSTM model. * With this study, it has been observed that the deep learning model achieves better performance than machine learning models. * EEG signals belonging to the GAMEEMO dataset were obtained with a portable EEG device. With this study, it has been observed that portable EEG devices are at least as successful as conventional devices. * It was observed that the discrete emotion model and the dimensional emotion model performed differently. The reason for choosing the GAMEEMO dataset is that it is a physiological dataset based on aural/visual stimuli (computer games). The aim of this study is to process biomedical signals to discover the effect of computer games on the players' personal mood (emotion).