# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 2 Related Works  
In this section, recent emotion recognition studies with EEG signals are included. In the study of [19], a new emotion dataset based on physiological brain signals was presented, called GAMEEMO. Four computer games (boring, quiet, horror, and funny) were used as visual/aural stimuli. Then, data were collected from 28 subjects using a device called EMOTIV EPOC with 14 channels. After collecting the data, they processed the collected data and the features were extracted by using the statistical method, DWT (discrete wavelet transformation), Hjorth, and others. Finally, MLPNN (multi-layer perceptron neural network), SVM, and kNN classifiers were applied to classify the discrete emotions and the arousal-valence model. In the study of [20], a discrete emotion model was used to classify the GAMEEMO dataset as a physiological database, and the features were extracted using the method of spectral entropy values of EEG signals, and then a bidirectional long-short term memory algorithm was used to classify the emotion. This study had 76.91% accuracy and 0.9 ROC (receiver operating characteristics). In the study of [21], a model based on an EEG was presented to detect real emotions, realistic emotions, and fake emotions. A model was created for rating real, fake, and neutral smiles. Features were extracted using three methods: DWT, EMD, and incorporating DWT into EMD. Then, kNN, SVM, and ANN (artificial neural network) were applied to classify the three emotions. The highest classification accuracy obtained from ANN, in the alpha and beta bands (94.3% and 84.1%, respectively). In the study of [22], an approach based on VMD was proposed to extract features from EEG signals and DNN (Deep Neural Network) was applied as a classifier. A dimensional emotion model was considered on the DEAP dataset. This study had a rating accuracy of 62.50% in valence and 61.25% in arousal. In the study of [23], the authors proposed a method for detecting personality traits by using physiological signals. Where the research team displayed a set of image and video stimuli on 18 subjects, data were collected from the participants in the experiment using a portable and wearable EEG device. The research team used the one-way ANOVA (analysis of variance) method to select the lowest set of features, then machine learning algorithms were applied including kNN, LR (logistic regression), NB (naive Bayes), and SVM. The research team obtained the following accuracy results: 89.24% with kNN, 61.11% with LR, 79.17% with NB, and 95.45% with SVM. Authors in [24] performed a binary classification to detect stress using brain signals. Theycollected data from the physical bank which consisted of 182 s of normal relaxation and 62 s of stress statuses. They calculated PSD (power spectral density) from the preprocessed data to extract the features using an FFT (fast Fourier transform). After extracting the features, they applied SVM and kNN algorithms to provide a classification. They obtained an accuracy of 99.42% from the FP1 EEG channel by using kNN, and 98.72% from the FP1 EEG channel with SVM. In the study of [25], the authors performed emotion recognition by using facial expressions. In the study, researchers obtained the emotion stimuli data from GAPED dataset. Researchers played music clips to stimulate the emotions of the participants in the experiment to discover the six basic emotions (joy, surprise, sadness, fear, disgust, anger) and the neutral state. The authors employed three types of SVM classifiers, including linear, RBF (radial basis function), and polynomial. At the end of the study, they achieved the best classification accuracy of 58% with a linear kernel. In the study of [26], the authors developed a new method for extracting emotions by combining HOG (oriented gradient) with GC (Granger causal), and TE (transfer entropy). SVM algorithm was applied to classify the emotional states of stress and calm.