Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorithms lead to loss of the representative details of brain activity and suffer from remaining artifacts because such algorithms only aim to minimize the temporal mean-squared-error (MSE) under generic penalties. Instead of using temporal MSE according to conventional mathematical models, this paper introduces a novel reconstruction algorithm based on generative adversarial networks with the Wasserstein distance (WGAN) and a temporal-spatial-frequency (TSF-MSE) loss function. The carefully designed TSF-MSE-based loss function reconstructs signals by computing the MSE from time-series features, common spatial pattern features, and power spectral density features. Promising reconstruction and classification results are obtained from three motor-related EEG signal datasets with different sampling rates and sensitivities. Our proposed method significantly improves classification performances of EEG signals reconstructions with the same sensitivity and the average classification accuracy improvements of EEG signals reconstruction with different sensitivities. By introducing the WGAN reconstruction model with TSF-MSE loss function, the proposed method is beneficial for the requirements of high classification performance and low cost and is convenient for the design of high-performance brain computer interface systems.

中文翻译：

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使用具有 Wasserstein 距离和时空频率损失的生成对抗网络重建 EEG 信号

基于脑电图 (EEG) 信号的应用面临着高分类性能与低成本的相互矛盾。这种矛盾的性质使得具有高采样率和灵敏度的 EEG 信号重建具有挑战性。传统的重建算法会导致大脑活动的代表性细节丢失，并遭受残留的伪影，因为此类算法仅旨在最小化通用惩罚下的时间均方误差 (MSE)。与根据传统数学模型使用时间 MSE 不同，本文介绍了一种基于具有 Wasserstein 距离 (WGAN) 和时空频率 (TSF-MSE) 损失函数的生成对抗网络的新型重建算法。精心设计的基于 TSF-MSE 的损失函数通过从时间序列特征、常见空间模式特征和功率谱密度特征计算 MSE 来重建信号。从具有不同采样率和灵敏度的三个与运动相关的 EEG 信号数据集获得了有希望的重建和分类结果。我们提出的方法显着提高了相同灵敏度的脑电信号重建的分类性能，以及不同灵敏度的脑电信号重建的平均分类精度提高。该方法通过引入具有TSF-MSE损失函数的WGAN重建模型，有利于满足高分类性能和低成本的要求，便于高性能脑机接口系统的设计。