## 8 Conclusions  
In this paper, we proposed a novel pattern approach based on PFP features for recognition of EEG-based multidimensional emotions. The EQA method is proposed to quantify the multidimensional emotional statue, questionnaire and semantic analysis about the correlation between emotions are obtained for objectively reflexing on the correlation between emotions. The EMER model is proposed to discover the criterial rules for recognition of multidimensional emotions, furthermore, the PFP features are extracted to classify the differenta of emotions. Lastly, the evaluation is conducted to verify the pattern approach on emotion recognition, also, the correlation between semantically similar emotions are analyzed. As a result, the novel pattern approach has proved its efficiency on recognition task.  
The overall prediction accuracy of all emotions on theta, alpha, beta and gamma bands have all reached above 92%, which proved that not only high frequency bands as beta and gamma are better for recognizing emotions, theta and alpha bands have also performed greatly. Moreover, each electrode of 32 channels has reached a rather steady accuracy, at averagely 93.5%, which indicts that the potential of single channel on recognizing different emotions. Particularly, theta band is outperformed unexpectedly when only based on single EEG channel, the accuracy ranges from 89% to 93%, and except for channel 5 (FC5), the rest channels have reached the accuracy above 90% separately.  
The recognition accuracy of 18 emotions have averagely reached above 90%, except for \"Intimacy\" and \"Manic\", where the recognition accuracy of \"Intimacy\" is 43% and 73% for \"Manic\". The confusion matrixes of those two emotions show that all wrongfully predicted signals are classified as \"Surprise\", moreover, those three emotions are semantically similar from the valence-arousal domains, which might indicates that the complex emotions as \"Intimacy\" and \"Manic\" might be overlapped in a certain extent to the basic emotion \"Surprise\". Other than those two emotions, our model can classify the rest emotions with a great result.  
Semantically similar groups of emotions are proved to appear similar partial fluctuation patterns in the EEG signals. Moreover, for groups such as: \"Fear\", \"Anxiety\" and \"Insecurity\"; \"Despair\" and \"Sadness\"; \"Surprise\", \"Intimacy\" and \"Manic\"; \"Confidence\" and \"Trust\", similar occurrence pattern of significant fluctuation patterns is observed in similar brain area.  
Above all, the EMER model has verified the novel pattern approach for recognition task from EEG signal when based on the PFP features with a high recognition accuracy on each frequency band and each single channel.  
Conceptualization, L.W.; methodology, H.L. and L.W.; software, T.Z. and W.L.; validation, T.Z. and H.L.; formal analysis, T.Z. and H.L.; investigation, L.W. and M.S.; resources, T.Z.; data curation, L.W. and M.S.; writing--original draft preparation, H.L.; writing--review and editing, L.W. and H.L.; visualization, H.L. and W.L.; supervision, T.Z.; funding acquisition, T.Z. and L.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Development Plan of Jilin Province, China (No.20200403039 SF).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from patients to publish this paper.  
**Data Availability Statement:** Publicly available dataset was analyzed in this study. This data can be found here: [[[http://www.eecs.qmul.ac.uk/mmv/datasets/deap/](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/)]([http://www.eecs.qmul.ac.uk/mmv/datasets/deap/](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/))].  
**Conflicts of Interest:** The authors declare no conflict of interest.