# EEG Data Augmentation for Emotion Recognition with a Task-Driven GAN
## 1 Introduction  
Emotions are ubiquitous in our daily lives and can affect or even determine our judgments and decisions. For instance, the authors of [1] show that visual complexity plays a positive role in impacting purchase intentions by affecting consumers' feelings of pleasantness and arousal; the findings in [2] suggest that the detection of positive emotion dysfunction could help to identify mild cognitive impairment not related to Alzheimer's disease patients.  
In recent years, human-machine communication has become more prevalent due to the popularization of mobile internet and artificial intelligence technology [3], making it more important for machines to have the ability to recognize and respond to human emotions. Emotion recognition usually refers to audio-visual speech recognition [4; 5], text emotion recognition [6], physiological signal emotion recognition [7], etc. Compared with other modalities, physiological signals are not influenced by human subjective factors, which can make emotion recognition more objective and reliable. EEG-based emotion recognition has received much attention in recent years due to the rapid development of brain-computer interface technology. EEGs can measure rhythmic oscillations, which contain rich emotional clues; thus, they can provide effective emotional state features.  
However, capturing emotional features usually requires a lot of available data due to the complexity of human emotions. Unlike audio and video, which are relatively simple to access, it is difficult to build a large-scale EEG dataset for the high price of EEG equipment. Data augmentation technology (as a promising method used to solve data scarcity issues [8], e.g., geometric transformation, including scaling, horizontal clippings, and rotation) has been widely used in image processing and computer vision tasks. Depth generation models, which are different from the signal level transformation, such as GAN [9], aim to learn thedeep-level representation of real distribution, so that downstream classification tasks can perform better.  
Among the current works on GAN-based EEG data augmentation, original EEG signals are generated through the methods proposed in [10; 11], which cannot be used directly for emotion recognition. Moreover, the impact on the performance of the downstream classification task was not taken into consideration in the data augmentation process in [10; 11; 12; 13], meaning these methods cannot guarantee the classifications of generated samples. Moreover, the network parameters of the GAN structure based on multiple generators [14] increase with the number of sample classifications, making it challenging to expand the modal. Furthermore, none of the works mentioned above retain the spatial information of multi-channel EEG signals during data processing or feature extraction. The method proposed in this paper aims to build a data augmentation network consisting of simple structures for EEG emotion recognition tasks, which can ensure the quality of the artificial data and effectively improve the performance of downstream classification tasks.