## 6 Experiment
### Evaluation of EMER Model  
In this section, the evaluation of EMER model is conducted to verify the pattern approach for recognition of EEG-based multidimensional emotions. An optimized SVM method is applied to evaluate this novel approach. Firstly, the emotional valence and arousal values on DEAP dataset are mapped into emotion labels based on EQA method, and 20 emotions are obtained including: \"Anger\", \"Anxiety\", \"Concentration\", \"Confidence\", \"Despair\", \"Disgust\", \"Fear\", \"Gratitude\", \"Hope\", \"Insecurity\", \"Intimacy\", \"Joy\", \"Manic\", \"Pain\", \"Passion\", \"Relaxation\", \"Sadness\", \"Surprise\", \"Tired\" and \"Trust\", however, emotion \"Shame\" is not detected from the DEAP dataset. Secondly, the data size of each emotion is balanced, where each emotion contains 250 EEG signals. Figure 3: Similarity Proportion of Emotion Subcategories. Thirdly, 5 PFP features of each signal are calculated according to the EMER model. Lastly, a 10-fold cross validation with 80% training set and 20% testing set is conducted on a grid research optimal SVM method. Moreover, PSD feature is calculated for a comparative experiment. The evaluation result on the feature performance can be seen from the Figure 4.  
It can be observed from Figure 4a that, by selecting the PFP features, the prediction accuracy is averagely above 90%, the alpha, beta and gamma bands have the average accuracy about 93.5%, however, theta band reveals an unstable prediction performance, but still, the averagely accuracy is about 92.25%, and channel 20 (F4 electrode) of theta band has detected the highest prediction performance of 93.99%. The result indicts that the PFP features can highly distinguish the differentia of those emotions. However, when dealing with single channel recognition task, it can be observed from Figure 4b that the PSD feature  
Figure 4: Evaluation on Feature Performance. (**a**) Performance on PFP Features; (**b**) Performance on PSD Feature. of which only reveals the distribution of power over frequency is not efficient enough to performance the recognition task, the maximum accuracy when applied with PSD is only 14%. Instead, the PFP features reserve more information on the partial variation in signal, which is differed from the analysis strategy of overall or average feature of each signal. Except for the accuracy, other evaluation indexes on the performance of EMER model are also analyzed as F1-score, Precision and Recall which listed at Table 5. It can be observed that EMER model has separately reached 0.9364 of F1-score, 0.9654 of precision and 0.9317 of recall, which has proved its efficiency. The average prediction accuracy of overall channels and frequency bands on each of emotions is shown at Table 6. It can be observed that the accuracy values of 18 emotions have reached above 90%, and the standard deviation values have revealed that each channel and each frequency band can be efficient on recognizing emotions. The recognition results on \"Hope\" and \"Sadness\" might be overfitting due to the original data size of those two emotions are rather a few, however, \"Surprise\" has the largest data size among 20 emotions of 242 signals are obtained originally, and as a result, the performance on \"Surprise\" has reached at averagely 96.62%, and the standard deviation is only 2.53. Furthermore, the prediction accuracy values on each single channel and each frequency band of 20 emotions are shown at Figure 5. It can be observed that most emotions have been accurately predicted with accuracy ranges from 80% to 100% with single channel and single band information. Interestingly, intimacy has the lowest prediction rate at averagely 43%, and manic is remained between 60% and 80%. However, from the confusion matrix of \"Intimacy\", all wrongfully predicted data are classified as \"Surprise\". The semantically similarity is further analyzed on those emotions, which shows that \"Intimacy\" and \"Surprise\" are closely mapped in valence-arousal domains where the distance is only 0.7.