## 2 Literature Review  
Researchers have proposed many different methods for recognizing emotions through EEG signals [2; 5]. The fundamental challenge of using EEG signals to detect human emotion lies in understanding how a particular emotional state is represented in the brain and applying the correct computational model to accurately identify that emotion, both automatically and in real time. Human emotion is naturally felt after a few seconds, not in a split second; therefore, it is more appropriate if the EEG signals used for the analysis have a set time frame to avoid analyzing the signals' amplitude as in the event-related potentials (ERP) technique. However, the ERP technique has been used in other emotion recognition research [6; 7; 8; 9] because the emotional stimuli were instantaneous pictures. On the other hand, to analyze EEG signals within a certain time duration for successful and productive use in an emotion recognition system, a signal processing technique is needed: one widely-adopted method is to calculate the power spectrum of the brain signals [4; 10; 11; 12; 13], and the results were an approximately 60-70% recognition rate, showing much room for improvement. Bispectrum analysis has been used as a signal processing technique for analyzing the EEG signal [14] and also used in medical research, such as in cases related to anesthesia [15; 16], meditation [17], vigilance tests [18] and recently in emotion recognition [19; 20], but the recognition rate result for the latter was somewhat lower than desired. [PERSON] [19] used seven features from bispectrum analysis taken from each of five EEG channels to recognize whether the subject was stressed; [PERSON] used a support vector machine (SVM) as the classifier and a genetic algorithm as the feature selection algorithm. [PERSON] (2017) used the bispectrum as a feature extraction method to identify four types of emotional states, namely the low and high arousal and the low and high valence from the data provided in the DEAP database ([PERSON] et al., 2016), using only two channels (Fp1 and Fp2) as the input signals and SVM as the classifier. There were five types of features calculated after the bispectrum analysis: normalized bispectral entropy (NBE), normalized bispectral squared entropy (NBSE), mean-magnitude of bispectrum (MMOB), first order spectral moment (FOSM) and second order spectral moment (SOSM). However, the use of a higher complexity feature calculation leads to an increased computation cost and may result in decreased accuracy. In this work, we propose a simpler method for feature extraction after the bispectrum values are calculated: to calculate the mean of bispectrum values after they are divided, by a filtering method, into several spatial regions, and using the percentage of the mean as the feature. Regarding the relationship between EEG and emotion, the prefrontal cortex area of the brain exhibits a strong correlation with human emotion. Nevertheless, through the feature selection process, some researchers have found that other EEG channels were better suited for emotion recognition ([PERSON], 2017; [PERSON], 2017). In the frequency domain, particular emotions are believed to affect specific frequency bands, such as alpha ([PERSON], 2017; [PERSON], 2018), gamma ([PERSON], 2018; [PERSON], 2018) and theta ([PERSON], 2018), and some researchers have incorporated all frequencies, delta, alpha, beta, theta and gamma ([PERSON], 2018; [PERSON], 2018), into their EEG research. In this work, we used all frequencies of brain signals and omitted none.