Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize EEG data. This network addresses several modeling challenges of simulating time-series EEG data including frequency artifacts and training instability. We further extended this network to a class-conditioned variant that also includes a classification branch to perform event-related classification. We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation (RSVP) experiment and demonstrated the validity of the generated samples. We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet.

中文翻译：

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使用Wasserstein生成对抗网络对EEG数据分布进行建模，以预测RSVP事件。

由于复杂的实验装置以及长时间佩戴会降低舒适度，因此难以获得脑电图（EEG）数据。这对于使用有限的EEG数据训练强大的深度学习模型提出了挑战。能够通过计算生成EEG数据可以解决此限制。我们提出了一种新颖的具有梯度罚分的Wasserstein生成对抗网络（WGAN-GP）来合成EEG数据。该网络解决了模拟时间序列脑电数据的几个建模挑战，包括频率伪影和训练不稳定性。我们进一步将此网络扩展为一个类条件变体，该变体还包括一个执行事件相关分类的分类分支。我们训练了建议的网络，以生成一个和64通道数据，类似于在快速串行视觉表示（RSVP）实验中常规看到的EEG信号，并证明了所生成样本的有效性。我们还测试了受试者内部跨会话分类性能以对RSVP目标事件进行分类，并表明具有条件的WGAN-GP可以在EEGNet上实现更高的事件分类性能。