## 1 Introduction
In these studies (2018,2019,2019), power spectral density (PSD) from EEG signals worked well for identifying emotional states. However, feature extraction usually generates high-dimensional and abundant features. Feature selection and dimension reduction are necessary to avoid over-specification and to reduce computational burden (2018). Compared to filter and wrapper methods for feature selection, the dimension reduction methods, e.g., principal component analysis (PCA), and Fisher linear discriminant (FLD), are more efficient. For further information about feature selection and dimension reduction, we refer the reader to (2018,2019). Many machine learning algorithms have been introduced as EEG-based emotion classifiers, such as support vector machine (SVM) (2018,2019), Naive Bayes (NB) (2018), K-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF),and artificial neural networks (ANN). Among these methods, SVM based on spectral features, e.g., PSD, is the most widely applied approach. In [25], SVM was used to classify the joy, sadness, anger, and pleasure feelings based on the EEG signals from 12 symmetric electrodes pairs. SVM was used in [26] for emotion recognition with the accuracies 32% and 37% in valence and arousal dimensions, respectively. A Gaussian NB in [27] was used to classify low/high valence, and arousal emotion with precision of 57.6% and 62.0%, respectively. Recently, deep learning (DL) methods have been introduced for EEG-based emotion classification [28; 29]. The study [30; 31] proposed deep belief network (DBN) to discriminate positive, neutral, and negative emotions. The experimental results show that DBN performs better than SVM and KNN. In [32], after an effective pre-processing method instead of traditional feature extraction methods, a hybrid neural network combining convolutional neural network (CNN) and recurrent neural network (RNN) is proposed to learn spatial-temporal representation from the pre-processed EEG recordings. The proposed pre-processing strategy improves the emotion recognition accuracies by about 33% and 30% for valence and arousal dimensions, respectively. In [33], a deep CNN (DCNN) model is introduced to learn discriminative representations from the combined features in the raw time domain, after normalization, and in the frequency domain. The obtained emotion classification accuracies are higher than the traditionally best bagging tree (BT) classifier. The study [34] proposed a hierarchical bidirectional gated recurrent unit (GRU) network with an attention mechanism. The proposed scheme learned more significant representation from EEG sequences and the accuracies obtained on cross-subject emotion classification task outperformed the long short time memory (LSTM) network by 4.2% and 4.6% in valence and arousal dimensions, respectively. Compared to traditional shallow methodologies, the DL models remove the signal pre-processing and feature extraction/selection progress, and are more suitable for affective representation [35; 36]. However, the DL methods cannot reveal the relationship between emotional states and EEG signals for being like a black box [37]. Moreover, the training of DL networks is extremely computationally time-consuming, which limits their practical applications in real-time emotion recognition [3]. As aforementioned, the field of affective computing has developed a lot over the past several years, including the incorporation of DL methodologies. However, the modeling and recognition of emotional states is still an unexplored problem [13; 14]. EEG-based emotion recognition is still faced with several challenges, including fuzzy boundaries between emotions. Note that logistic regression (LR) [38] has been widely used as a statistical learning model in pattern recognition and machine learning, as well as in EEG signal processing. In [39], LR trained with EEG power spectral features was used for automatic epilepsy diagnosis.