# EEG Data Augmentation for Emotion Recognition with a Task-Driven GAN
## 3 The Proposed Method
### EEG Data Augmentation for Emotion Recognition
#### 3.2.2 The Task-Driven CWGAN
In this part, SVM and three different neural networks were selected as classifiers to classify emotions in two emotional dimensions: arousal and valence. It can be seen that compared with the original CWGAN, the proposed method (the CWGAN-T in Table 3) can effectively improve the accuracy of classification tasks; the accuracy in the two emotional dimensions improved by 1.5-5.5% and 2-5.2%, respectively. Figure 5: Visualization with UMAP to observe the distribution of the original data (a), the data generated by CWGAN (b), and the data generated by the proposed method (c). ## 5 Conclusions

This paper presents a task-driven EEG data augmentation network based on CWGAN, which aims to generate artificial EEG data with clearer classifications. The multi-channel EEG differential entropy feature matrix that retains the spatial distribution information of channels was extracted as the data augmentation target, and the emotion classifier was introduced into the original CWGAN structure. In each iterative training process of the generator and discriminator, the classifier guides the generator to generate data with clearer classifications by providing classification loss-related gradient, so as to improve the quality of generated data and effectively improve the accuracy of emotion recognition task. Finally, the proposed method was evaluated in two aspects, namely, the quality of data and the performance improvement of downstream tasks. The Wasserstein distance, MMD (maximum mean difference), and visualization with reduced dimension (UMAP) were used to evaluate the data quality. The results show that the proposed method improved the distribution similarity compared with the original CWGAN in the emotional dimensions of arousal and valence. It can be observed from the visualization that the classifier's introduction of a regularization item to the generator loss resulted in the cleared classification of the generated samples. Four different classifiers, including SVM and Lenet, were used to evaluate the performances of downstream classification tasks after data augmentation. The results show that compared with the original CWGAN, the proposed method can improve the accuracy of classification tasks more effectively, and the accuracy in the emotional dimensions of arousal and valence improved by 1.5-5.5% and 2-5.2%, respectively. However, some problems from this study will need to be further discussed. For instance, qualifying the hyperparameter \(\rho\) in Formula (4) can be improved, and penalty terms related to data diversity can be added to the loss function to prevent the generated data from being too centralized and single. These problems will be addressed in future research. Conceptualization, Q.L. ; Methodology, Q.L. ; Software, Q.L. ; Validation, Q.L. ; Investigation, Q.L., J.H. and Y.G. ; Resources, J.H. and Y.G. ; Writing--original draft, Q.L. ; Writing--review & editing, J.H. and Y.G. ; Supervision, J.H. ; Project administration, J.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The public dataset used in this experiment can be obtained 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/)) (accessed on 7 February 2023). The authors declare no conflict of interest.