## 7 Discussion  
The EMER model performed splendidly on recognition of emotions, this novel approach gives an insight on emotion recognition that the partial fluctuation pattern of EEG signal is suitable on interpreting emotions, when experiencing certain emotion, the EEG signals occurred significant change patterns, which can be more directly on recognizing emotional status. And among the 20 emotion categories, each emotion has detected significant and particular change patterns even for those complex emotions, as for some semantically similar emotions which are regarded as partially overlapped in physiological signal, EMER model can still distinguish them correctly based on PFP features. Moreover, the 4 frequency bands as alpha, beta, gamma and theta have proved its efficiency on recognition emotions when applied with PFP features, which reveals the potential of theta and alpha bands on recognizing emotions.  
Also, some interesting phenomena are occurred when distinguishing similar emotions. The prediction results of \"Intimacy\" and \"Manic\" which indict that: some basic emotions such as \"Surprise\" might have a greater range of affection on those similar overlapped complex emotions, for example, for \"Manic\" when it has a farther distance with \"Surprise\", the prediction accuracy has increased compared to the prediction accuracy of \"Intimacy\" which is much closer to \"Surprise\". Those complex emotions which are acquired by learning, external factors such as morality might have different influence on individuals when cognizing emotions [47], this theory can be also applied to the result of \"Gratitude\", which is semantically similar with \"Confidence\" and \"Trust\", however, inactivity in brain area is observed which is opposite from \"Confidence\" and \"Trust\". Another possibility might be data itself that the distribution of the data size under each emotion is uneven, which causes that some emotions have detected less criterial rules for classifying the differentia among similar emotions. For example, \"Surprise\" has the largest data size compared to other emotions, 242 signals are obtained originally, therefore, more criterial rules are discovered to generate more significant PFP features, as a result, it has reached an average accuracy of 96.62%. Furthermore, the number of criterial rules for \"Manic\" and \"Intimacy\" are relatively lesser than \"Surprise\", the significance of those two emotions might not be fully discovered.  
Our proposed approach is to discover the relevance on the fluctuation patterns among EEG signals for emotion recognition task in each frequency bands by using single channel information, which is differed from the general strategies which focused on particularly high frequency bands or optimization of channel selection for recognition of EEG-based  
Figure 6: 2D Scalp Map of Brain Activities.  
emotions. Furtherly, the recognition task is extended into 20 different emotion categories, the differentia of those emotions can be successfully distinguished based on the PFP features. Moreover, the semantically similarity among emotions is proved as physiologically similar among similar emotion groups. However, the limitations in differentiation of similar emotions are mainly due to the overlapping issue occurred among complex emotions, which is restricted by the data size of each emotion. Therefore, we will continuously collect more EEG signals by our own in our future work for further analysis. Moreover, the external factors that might have an influence on those complex emotions will be taken into consideration.