## 2 Related Work
[23] conducted an experiment on the classification of emotion based on the recorded signals from FP1 channel with the stimuli of horror and relaxing movies. Three emotions status are observed as neutral, relaxation and fear. And by using the KNN classification method, the result shows that with 46 features, the accuracy has reached averagely 94%. [PERSON] et al. [24] used a correlation analysis and frequency-based filtering method on emotion recognition of happy, fear, sad and relax status from single-channel signals, electrodes of FP1, FP2, F3, F4, F7, F8, T3, T4, T5 and T6 are selected as single-bipolar channel, furtherly, the result shows that the proposed method has an overall accuracy of 90.63%. [PERSON] et al. [25] analyzed the functional connectivity pattern of emotion recognition based on EEG signals and eye movements, also, pointed out that the strength feature is proved to be outperformed than the differential entropy in single channel, as a result, that emotions of happiness, disgust, fear, sadness, and neutrality have detected significant functional connectivity, and the accuracy on DEAP, SEED and SEED-V are separately reached at 86%, 95% and 84%. [PERSON] et al. [26] conducted an experiment that used the single channel signal to detect major depressive disorder with machine learning methods, the result shows that signals collected from FP1 electrode is outperformed than FP2 electrode in prescreening of major depression disorder, and the Classification and Regression Tree combining with genetic algorithm (CART-GA) has a great performance than other machine learning methods. [PERSON] et al. [27] proposed a dynamical graph convolutional neural networks on EEG emotion recognition, by analyzing the intrinsic correlation between different electrodes, discriminative features can be discovered and performed efficiently on emotion recognition task. Generally, studies based on emotion types are mainly discrete or quantified in valence-arousal domains, for some experiments only recorded the objectively self-rating emotion values instead of emotion labels. [PERSON] et al. [28] analyzed four emotions as anger, sad, happy and surprise with the characteristic of different frequency bands, and the result shows that anger is associated with right side of brain in theta band, sad is related to right posterior region of brain with delta and theta bands, happy is noticeable in the middle region with alpha band, and surprise is clearly observed in delta and theta bands. [PERSON] et al. [29] analyzed ten positive emotions in EEG signal as: amusement, ave, gratitude, hope, inspiration, interest, joy, love, pride and serenity, cluster analysis is conducted on those ten emotions with three cluster groups as encouragement, playfulness and harmony based on the objective rating of valence, arousal, familiarity and liking. As a result, the playfulness group has reached the highest accuracy of nearly 90%, while encouragement and harmony have separately 84% and 85% of accuracy. [PERSON] et al. [30] used KNN to cluster the emotional value of valence-arousal in DEAP dataset as several emotion categories, and analyzed the performance of channel combination with number of 10, 14, 18 and 32. As a result, 32 number of channels has the highest accuracy. Generally, studies of EEG-based emotion recognition mainly analyzed several emotions such as neutral, positive and negative, or happy, sad, anger and etc. [31; 32; 33; 34; 35]. Other than the self-conducted experiments to analyze EEG-based emotions, the DEAP and SEED datasets are wildly applied [36; 37; 38; 39; 40], SEED datasets have given the exact emotion labels, but the categories of emotions are generally within 5, on the other hand, DEAP dataset uses valence and arousal to quantify the emotion value, and cluster analysis methods are commonly applied to group those coordinate into several emotion labels.