## 2 Literature Review  
The effectiveness of different ML models in [4; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] has been proven in the applications of emotion detection using EEG signals. Obviously, the utility of ML models requires feature extraction and selection using EEG signals to identify the most informative feature subsets for the improvement of the final detection performance. Indeed, the Support Vector Machine (SVM) is used as the main model for the method development in [14] using features extracted from EEG signals in time, frequency, and time-frequency domains. Here, the most relevant feature subset is selected by a modified particle swarm optimization method with multistage linearly-decreasing inertia weight for the input of the SVM model, which contributes to the significant improvement of multiple emotion recognition. In [15], 2 models, namely K-nearest neighbors (KNN) and Artificial Neural Networks (ANN) use the entropy and energy features extracted from the sub-bands, which are generated by the Fourier-Bessel series expansion-based empirical wavelet transform. The preselection of 10 channels from the frontal lobes in combination with 3 feature selection algorithms such as neighborhood component analysis, Relieff, and minimum-Redundancy-Maximum-Relevance (mRMR) results in the input feature dimension reduction while the final detection performance of the proposed algorithm is improved certainly. A GA based evolution algorithm is applied for the feature selection, which releases the most relevant feature subsets used as the input of KNN, Random Forest (RF), ANN models [19] and SVM [20] for the estimation of their performance in terms of emotion detection. In [19], the effectiveness of the GA has been proven clearly with a small percentage of the selected features, approximately 2% out of the total, corresponding to the model, which shows better detection performance than that using the entire input features. The authors of [20] consider 3 original feature sets for the GA-based feature selection, which leads to the feature proportions of 10%, 12%, and 25% chosen from the 3 above feature sets. In [21], the performance investigation of the variational mode decomposition and the DWT is indicated by the effectiveness comparisons of sub-signals in different frequency bands for feature extraction. Additionally, the non-dominated sorting genetic algorithm (NSGA-II) is implemented for the identification of the most effective channels and features in combination with different ensemble learning algorithms. As a result, a subset of 8 features extracted from 7 channels, which are selected by the variational mode decomposition-based feature extraction combined with the NSGA-II, and a SVM model are proposed as the final emotion recognition algorithm. Furthermore, the principal component analysis is deployed for the dimension reduction of the feature matrix, which is constructed by the power spectral density of the individual sub-bands. In [22], the performance superiority is confirmed definitely for the quantum-based SVM in comparison with the conventional SVM models. The authors of [4] also employ the principal component analysis for the dimension reduction of the input feature space including sub-band power, energy, and others in the time domain. Here, the wavelet-based atomic function and ML models are considered for the signal decomposition and classification. To improve the detection performance and generalization ability of the ensemble learning models, the overlapping 4s EEG segments are used for the extraction of numerous features, which are then reduced by the SVM model using L1-norm penalty-based feature selection. In [23], the detection performance of various ensemble learning models is compared for the proposal of the MOSNK model as the final algorithm using all of the extracted features. There is no requirement of feature extraction, and selection using human expert knowledge or conventional expertise for the application of the DL models.