# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
###### Abstract  
Emotion can be defined as a voluntary or involuntary reaction to external factors. People express their emotions through actions, such as words, sounds, facial expressions, and body language. However, emotions expressed in such actions are sometimes manipulated by people and real feelings cannot be conveyed clearly. Therefore, understanding and analyzing emotions is essential. Recently, emotion analysis studies based on EEG signals appear to be in the foreground, due to the more reliable data collected. In this study, emotion analysis based on EEG signals was performed and a deep learning model was proposed. The study consists of four stages. In the first stage, EEG data were obtained from the GAMEEMO dataset. In the second stage, EEG signals were transformed with both VMD (variation mode decomposition) and EMD (empirical mode decomposition), and a total of 14 (nine from EMD, five from VMD) IMFs were obtained from each signal. In the third stage, statistical features were obtained from IMFs and maximum value, minimum value, and average values were used for this. In the last stage, both binary-class and multi-class classifications were made. The proposed deep learning model is compared with kNN (k nearest neighbor), SVM (support vector machines), and RF (random forest). At the end of the study, an accuracy of 70.89% in binary-class classification and 90.33% in multi-class classification was obtained with the proposed deep learning method. EEG; emotion recognition; deep learning; signal processing +
Footnote †: journal: Medical  
method used to obtain emotions is the analysis of facial expressions. In this method, the subjects stand in front of the camera and their reactions to the stimuli are observed [6]. Similarly, emotions are examined with body language and physical movements. For example, while an angry person joins his/her lips, a bored person looks out of the window or bang his/her feet [7]. However, the constant manipulation of the emotions obtained by these methods may cause the data to be inaccurate and unreliable. People can easily manipulate their gestures, tones, and movements [8]. For these reasons, the need for a more reliable system has arisen and the importance of physiological signals has increased. The most commonly used signals in emotion recognition studies are EEG (electroencephalography), ECG (electrocardiography), PPG (photoplethysmography), GSR (galvanic skin response), and SKT (skin temperature) [9]. However, the most preferred one among these methods is the EEG signals. The main reasons for this situation are that it is easy to use, cost-efficient compared to other methods, and has portable and wearable technology [10]. EEG is a technique for collecting data from a person by placing a wearable device around the head in a non-invasive way. This device contains electrodes placed around the head according to the 10-20 system, as given in Figure 1.  
This device monitors brain activity and records signals from the brain. These devices transmit the recorded signals to computers via wired or wireless communications for the purpose of analysis. EEG is an easy and inexpensive method that has been used to record brain activity, and it has also proven that it is through brain activity that emotions can be detected. Measuring brain activity via EEG devices helped scientists understand the emotions of people with disabilities whose emotions are difficult to detect through their facial expressions [13]. There are two types of emotion models in the literature, discrete and dimensional. The discrete emotion model consists of eight (anger, fear, sadness, disgust, surprise, anticipation, trust, and joy) basic emotions, including positive and negative emotions [14]. On the contrary, in the dimensional model, emotions are expressed not by their names but by their position in the arousal-valence plane [15]. Emotions are placed in four different zones on the arousal-valence plane. The valence plane indicates negative and positive emotions.