## 2 Literature Review
Moreover, representative features are reinforced by the capability of the deep learning process, which makes the DL models outstanding in comparison with the ML techniques. Indeed, the authors of [24] use 2-dimension power topographic maps constructed by the power spectral density features of 5 sub-bands, which are then used as the input of the spatial-temporal information learning network including spatial feature extraction based-convolutional neural network (CNN) and temporal context learning based-long short term memory (LSTM). The power topographic maps effectively represent the most vital brain areas while the temporal context learning based-LSTM captures significant dependencies of EEG frames. The study in [25] introduces a hybrid unsupervised deep convolutional recurrent generative adversarial network designed for EEG feature characterization and fusion namely EEGFuseNet. This network plays a role in automatic deep feature extraction, which captures spatial and temporal dynamics, making them more generic and independent of specific EEG tasks. In [26], the adjacency matrices calculated by the phase locking value (PLV) method are adopted for the interaction representative of sub-bands generated from 20s EEG segments. Then, the CNN model is fed with the above matrices as the input for further classification. Clearly, the final detection performance of the proposed algorithm is improved significantly with the use of group PLV compared with individual PLV. Besides, the authors in [27] propose a novel deep learning framework for EEG-based emotion recognition, which includes a spatio-temporal representation of multichannel EEG signals namely TQWT-feature block sequences and a hybrid convolutional recurrent neural network. Here, a lightweight CNN and a LSTM network capture spatial information and temporal dependencies, respectively, are the 2 main elements of the hybrid convolutional recurrent neural network, which proves the essential ability of emotion recognition. Various ensemble learning and DL models are investigated in [28] using a large number of features extracted from 5 sub-bands. Superior detection performance is released for a hybrid paradigm in comparison with that produced by ensemble learning or individual DL models. In [29], the proposed method includes a graph convolutional network using differential entropy-based 3-dimension spatial-spectral features extracted from various sub-bands by the Welch method. Here, the adjacency matrix calculation is based on the above features and contextual loss computed by trainable adjacency relation in combination with the graph convolutional network model. The detection comparisons between the ML and bidirectional LSTM are provided in [30] using 4 featuresextracted in time, frequency, and entropy domains. The structure of the bidirectional LSTM is optimized by the investigation of hyperparameters, which produce better performance compared with that released by the ML models. To improve the emotion detection performance of the proposed algorithm, feature and channel selection are considered for the optimization of the extracted features as the input of DL models. In [2; 17], adjustable channel selection based on attention distribution in the graph structure and 10 frontal lobe channels are given for the construction of scalogram images, and intra- and inter-channel EEG features. Moreover, the graph convolution models [2] and the deep CNN [17] are proposed as the emotion classifiers in which the classification performance of the former is slightly higher than that of the latter. The scalogram images are also used in [31] generated by the wavelet transform to convert EEG signals from time-scale representation into these images using different sub-bands. Then, the preselection model namely GoogLeNet is applied for emotion recognition using the scalogram images, which produces a relatively high performance for the multiple emotion classification.