Electroencephalography (EEG)-based affective computing has a scarcity problem. As a result, it is difficult to build effective, highly accurate and stable models using machine learning algorithms, especially deep learning models. Data augmentation has recently shown performance improvements in deep learning models with increased accuracy, stability and reduced overfitting. In this paper, we propose a novel data augmentation framework, named the generative adversarial network-based self-supervised data augmentation (GANSER). As the first to combine adversarial training with self-supervised learning for EEG-based emotion recognition, the proposed framework generates high-quality and high-diversity simulated EEG samples. In particular, we utilize adversarial training to learn an EEG generator and force the generated EEG signals to approximate the distribution of real samples, ensuring the quality of the augmented samples. A transformation operation is employed to mask parts of the EEG signals and force the generator to synthesize potential EEG signals based on the unmasked parts to produce a wide variety of samples. A masking possibility during transformation is introduced as prior knowledge to generalize the classifier for the augmented sample space. Finally, numerous experiments demonstrate that our proposed method can improve emotion recognition with an increase in performance and achieve state-of-the-art results.

中文翻译：

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GANSER：基于脑电图的情绪识别的自监督数据增强框架

基于脑电图（EEG）的情感计算存在稀缺问题。因此，利用机器学习算法，尤其是深度学习模型，很难建立有效、高精度和稳定的模型。数据增强最近显示深度学习模型的性能得到了提高，准确性、稳定性得到提高，并且过度拟合得到了减少。在本文中，我们提出了一种新颖的数据增强框架，称为基于生成对抗网络的自监督数据增强（GANSER）。作为第一个将对抗性训练与自监督学习相结合的基于脑电图的情感识别框架，该框架生成了高质量和高多样性的模拟脑电图样本。尤其，我们利用对抗性训练来学习脑电图生成器，并迫使生成的脑电图信号接近真实样本的分布，从而确保增强样本的质量。采用变换操作来屏蔽部分脑电图信号，并迫使发生器根据未屏蔽部分合成潜在的脑电图信号，以产生各种样本。引入变换期间的掩蔽可能性作为先验知识，以概括增强样本空间的分类器。最后，大量实验表明，我们提出的方法可以通过提高性能来改善情绪识别，并取得最先进的结果。采用变换操作来屏蔽部分脑电图信号，并迫使发生器根据未屏蔽部分合成潜在的脑电图信号，以产生各种样本。引入变换期间的掩蔽可能性作为先验知识，以概括增强样本空间的分类器。最后，大量实验表明，我们提出的方法可以通过提高性能来改善情绪识别，并取得最先进的结果。采用变换操作来屏蔽部分脑电图信号，并迫使发生器根据未屏蔽部分合成潜在的脑电图信号，以产生各种样本。引入变换期间的掩蔽可能性作为先验知识，以概括增强样本空间的分类器。最后，大量实验表明，我们提出的方法可以通过提高性能来改善情绪识别，并取得最先进的结果。