## 2 Related Work  
Currently, most studies on EEG-based emotion recognition are focusing on selection of features, classification methods, optimization of channel selection and emotion categories which can be recognized by EEG signal. Related studies have proved that diverse features and multiple machine learning methods are efficient on the classification task. [PERSON] et al. [15] proposed a combination method of spatial pattern and wavelet packet decomposition on recognition of EEG-basedemotions, which is aim for reducing fluctuations and differences on the signals, the data contain with sad and happy labels, as a result, it reaches accuracy of 86.2%. [PERSON] et al. [16] compared several performances of machine learning methods on recognition of EEG-based emotions, including support vector machine (SVM), k-nearest neighbor (KNN), linear discriminant analysis (LDA), logistic regression (LR) and decision tree (DT), and as a result, SVM has a better performance with accuracy ranges from 54% to 71%. [PERSON] et al. [17] proposed a combination method contains with wavelet energy, modified energy and wavelet entropy features on detection of emotion for analysis the possibility of ambient assisted living. The labels of emotion are divided into four types as HVHA, HVLA, LVHA and LVLA according to the distribution of valence-arousal model. And in the experiment, three classification methods including quadratic discriminant analysis (QDA), KNN, SVM are applied, and as a result, SVM has a higher performance of the accuracy rate on recognition of the emotion at averagely 83%. [PERSON] et al. [18] proposed a feature selection approach due to the combination of heterogeneous features might on the other hand reduce the accuracy, 440 features are adopted into the feature ranking method as transfer recursive feature elimination (T-RFE), and result shows that, only 10 features are significant for classification of emotions. [PERSON] et al. [19] used different classification methods on the feature extraction of EEG as fuzzy C-means (FCM) and fuzzy K-means (FKM), six emotions including: anger, disgust, fear, happy, sad and surprise are labeled in the dataset, and features are selected with energy and entropy, as a result, those methods performed well on the clustering emotions. [PERSON] et al. [20] explored the performance of different features on classification of emotions, 18 features of two types including time-frequency domain feature and non-linear dynamical system features are analyzed on different datasets. As a result, Hjorth parameter: mobility, Hjorth parameter: complexity and maximum power spectral frequency performed well in beta band, and the combination of linear features is better compared to non-linear features in each band. [PERSON] et al. [21] proposed a feature selection approach based on bag of deep features, which can reduce the features of data according to the k-mean clustering, as a result, when the clustering group is 10, the SVM has reached separately 77% and 93% of accuracy on recognition of emotions based on DEAP dataset and SEED dataset, where DEAP dataset contains with EEG signals with self-rating emotion values of valence-arousal domain, and SEED dataset contains with EEG signals with three different emotion labels as \"Positive\", \"Negative\" and \"Neutral\". [PERSON] et al. [22] proposed a method based on empirical mode decomposition (EMD) to extract the multidimensional information features on Intrinsic Mode Functions (IMFs) for EEG emotion recognition, and result shows that those three features as the first difference of IMF's times series, the first difference of IMF's phase and the normalized energy of IMF are proved to be efficient on recognition of EEG-based emotion based on DEAP dataset. Mainly, multi-channel is applied on EEG-based emotion recognition due to the combination of different channel can outcome great results. However, single channel is more application-oriented which has an affordable price and convenient usage in daily life. [PERSON] et al.