# EEG Data Augmentation for Emotion Recognition with a Task-Driven GAN
## 2 Related Works
### Data Augmentation Based on GAN
#### 2.2.2 EEG Data Augmentation Based on GAN  
[PERSON] et al. generated original EEG signals with GAN for the first [10]. They improved WGAN-GP to ensure the performance of the network when Pr and Pf were similar, so as to generate single-channel EEG data that were close to the real data in both time and frequency domains. [PERSON] et al. introduced data augmentation based on CWGAN to the field of EEG-based emotion recognition for the first time [12]. In their work, the distribution of the differential entropy (DE) feature of real EEG signals was learned by CWGAN to generate DE featured with labels to conduct data augmentation on the EEG dataset. The generated EEG features (DE) were evaluated by discriminator loss, maximum mean error (MMD), and two-dimensional mapping. High-quality data after the evaluation were added to the original dataset. Finally, a support vector machine (SVM) was introduced to evaluate the improvement of emotion recognition performance after data augmentation. In the latter study [13], [PERSON] et al. augmented the EEG data of SEED [19] and DEAP [20] by using the generation model based on GAN and VAE, respectively, and verified the classification performance by using SVM and the deep neural network, respectively. [PERSON] et al. designed a generation network based on DCWGAN [24] to generate raw EEG data [11], and used MLP, SVM, and KNN to evaluate the algorithm performance. Regarding CWGAN, [PERSON] et al. used multiple input generators to obtain more feature information so as to generate more diverse and more categorical EEG features (DE features) [14]. The generator part consists of multiple input layers and a parameter-sharing layer. Each input layer corresponds to one EEG label category (for example, the SEED dataset labels used in this study are divided into three classifications, so three input layers are required). Each input layer combines random noise with multiple generators of various labels to generate data with clear classification, which is conducive to improving the performance of downstream classification tasks. However, under the multi-generator structure, the number of input layers will increase with the increase in the number of data label classifications, resulting in a growing network scale.  
Inspired by research in the field of computer vision [25; 26], we propose a task-driven CWGAN that incorporates a classifier during generative adversarial training to generate data with clear classifications. At the same time, compared with the structures of multiple generators, this is easier to expand for situations involving multiple classifications.  
The main contributions of this paper are as follows:  
1. The multi-channel EEG differential entropy feature matrix, which retains the spatial distribution information of channels, was extracted as the data augmentation target.
2. For emotion recognition research, this is conducive to the subsequent classification work, i.e., to retain as much spatial information of the data as possible in the feature extraction process. In this study, the augmentation target is the multi-channel EEG differential entropy feature matrix that retains the channel spatial distribution information rather than the original EEG signal.
3. A task-driven data augmentation method for emotion recognition based on GAN is proposed.
4. We introduce the classifier into the EEG data augmentation network based on GAN. Both the discriminator and the classifier will provide gradients for parameter optimization. The former promotes the generation of realistic EEG data, and the latter ensures that the generated data will help to classify the performance.
5. We evaluate the performance of the proposed method from two aspects.
6. The Wasserstein distance, MMD (maximum mean difference), and reduced dimension visualization (UMAP) methods are used to evaluate the data quality. Four different classifiers, including SVM and Lenet, are used to enhance the accuracy of downstream classification tasks by enhancing the emotional classification evaluation data.