EEG signals combined with deep learning play an important role in the study of human–computer interaction. However, the limited dataset makes it challenging to study EEG signals using deep learning methods. Inspired by the GAN network in image generation, this paper presents an improved generative adversarial network model L-C-WGAN-GP to generate artificial EEG data to augment training sets and improve the application of BCI in various fields. The generator consists of a long short-term memory (LSTM) network and the discriminator consists of a convolutional neural network (CNN) which uses the gradient penalty-based Wasserstein distance as the loss function in model training. The model can learn the statistical features of EEG signals and generate EEG data that approximate real samples. In addition, the performance of the compressed sensing reconstruction model can be improved by using augmented datasets. Experiments show that, compared with the existing advanced data amplification techniques, the proposed model produces EEG signals closer to the real EEG signals as measured by RMSE, FD and WTD indicators. In addition, in the compressed reconstruction of EEG signals, adding the new data reduces the loss by about 15% compared with the original data, which greatly improves the reconstruction accuracy of the EEG signals’ compressed sensing.

中文翻译：

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基于改进生成对抗网络的脑电信号数据增强

脑电信号与深度学习的结合在人机交互的研究中发挥着重要作用。然而，有限的数据集使得使用深度学习方法研究脑电图信号具有挑战性。受图像生成中的 GAN 网络的启发，本文提出了一种改进的生成对抗网络模型 LC-WGAN-GP，用于生成人工 EEG 数据以扩充训练集并提高 BCI 在各个领域的应用。生成器由长短期记忆（LSTM）网络组成，判别器由卷积神经网络（CNN）组成，在模型训练中使用基于梯度惩罚的 Wasserstein 距离作为损失函数。该模型可以学习脑电信号的统计特征并生成近似真实样本的脑电数据。此外，可以通过使用增强数据集来提高压缩感知重建模型的性能。实验表明，与现有的先进数据放大技术相比，所提出的模型产生的脑电信号更接近真实脑电信号（如 RMSE、FD 和 WTD 指标测量的）。此外，在脑电信号的压缩重建中，新数据的加入比原始数据减少了约15%的损失，大大提高了脑电信号压缩感知的重建精度。