EEG-based emotion recognition has attracted substantial attention from researchers due to its extensive application prospects, and substantial progress has been made in feature extraction and classification modelling from EEG data. However, insufficient high-quality training data are available for building EEG-based emotion recognition models via machine learning or deep learning methods. The artificial generation of high-quality data is an effective approach for overcoming this problem. In this paper, a multi-generator conditional Wasserstein GAN method is proposed for the generation of high-quality artificial that covers a more comprehensive distribution of real data through the use of various generators. Experimental results demonstrate that the artificial data that are generated by the proposed model can effectively improve the performance of emotion classification models that are based on EEG.

基于EEG的情绪识别由于其广阔的应用前景而引起了研究人员的广泛关注，并且在从EEG数据进行特征提取和分类建模方面已经取得了实质性进展。但是，没有足够的高质量训练数据可用于通过机器学习或深度学习方法构建基于EEG的情绪识别模型。人工生成高质量数据是解决此问题的有效方法。在本文中，提出了一种多发生器条件Wasserstein GAN方法，用于生成高质量的人工模型，该模型通过使用各种发生器涵盖了更全面的真实数据分布。