# 4-Class MI-EEG Signal Generation and Recognition with CVAE-GAN
## 6 Conclusions and Future Work  
This paper studied the application of the CVAE-GAN in the classified motor imagery EEG generation with the various prospective usages in BCI systems, for instance, reconstruction of the corrupted data and non-homologous data augmentation. In order to achieve high quality generation under the 4-class conditional MI-task EEG, a combination of latent representation and the adversarial network is proposed to learn subject-invariant representations and to make the generated samples approaching the reals. Compared with other generated models on the public and private dataset, the proposed approach generated EEG samples from different subjects according to its MI-task, and experimental results show an effectiveness of the CVAE-GAN. In experimental evaluation, the training based on data augmentation with the proposed framework enhances the performance of MI-EEG recognition.  
In the future, this study should be further continued to evaluate how the generated EEG data affects the performance of the BCI system and transfer learning approach that explore and develop some shared structure in the classified EEG data.  
Figure 10: Classification performance of confusion matrices on real and synthetic samples.  
**Author Contributions:** Conceptualization, J.Y.; methodology, J.Y.; formal analysis, T.S.; writing--original draft preparation, J.Y., H.Y. and Y.S.; writing--review and editing, J.Y., H.Y. and Y.S.; supervision, Z.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Yunnan Reserve Talents of Young and Middle-aged Academic and Technical Leaders ([PERSON], 2018), Yunnan Young Top Talents of Ten Thousand Plan ([PERSON], [PERSON], Yunnan Social Development No. 2018 73).  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board.  
**Informed Consent Statement:** All subjects involved in this study gave their informed consent. Institutional review board approval of our hospital was obtained for this study.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Acknowledgments:** The research was totally supported and sponsored by the following project: Introduction of Talent Research Startup Fund Project of Kunming University of Science and Technology under Program Approval Number K8 SY201903028, National natural science foundation of China under Program Approval Number 31760281; Yunnan Reserve Talents of Young and Middle-aged Academic and Technical Leaders ([PERSON], 2018), Yunnan Young Top Talents of Ten Thousands Plan ([PERSON], [PERSON], Yunnan Social Development No. 2018 73). Furthermore, we are grateful for the development of Keras API which made the implementation of deep learning algorithms easier.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.