Electroencephalogram (EEG) signals recorded from the ambulatory systems are mostly contaminated by various artifacts like, electrooculogram (EOG), motion artifacts (MA) and electrocardiogram (ECG) artifacts. These artifacts limit the accuracy in further analysis of EEG in practise. So far, several existing methods have been proposed with the combination of decomposition techniques and independent component analysis (ICA) to remove single artifacts and only few methods to remove multiple artifacts from the single channel EEG. As improperly denoised EEG signals can result in wrong diagnosis, in this work, Singular Spectrum Analysis (SSA) and ICA are jointly combined with Generalized Moreau Envelope Total Variation (GMETV) technique to simultaneously remove combinations of different artifacts from single channel EEG. In this work, the SSA is used to decompose the contaminated single channel EEG, while the ICA is employed to separate the various hidden sources as independent components (ICs). Although the ICA is adequate in source separation, there is still, some essential EEG signal data appearing as artifact in the IC. Hence, eliminating this would allow EEG signal information to be lost. The GMETV approach is proposed in this paper, to estimate the actual artifacts in order to address these issues. The estimated actual artifacts are subtracted from the artifact ICs providing the residue of wanted component of EEG. This residue is added back to the remaining ICs, to obtain the denoised EEG. Simulation results demonstrated that the proposed technique performs better compared to the existing techniques. The Relative Root Mean Square Error (RRMSE) is reduced by 12.02% and 7.22% compared to SSA-ICA and SSA-ICA-thresholding respectively. Similarly, the Correlation Coefficient (CC) is increased by 21.48% and 8.25% with respect to SSA-ICA and SSA-ICA-thresholding respectively.

从动态系统记录的脑电图 (EEG) 信号大多受到各种伪影的污染，如眼电图 (EOG)、运动伪影 (MA) 和心电图 (ECG) 伪影。这些伪影限制了在实践中进一步分析 EEG 的准确性。到目前为止，已经提出了几种现有方法，结合分解技术和独立分量分析 (ICA) 来去除单个伪影，而从单通道 EEG 中去除多个伪影的方法很少。由于去噪不当的 EEG 信号会导致错误的诊断，在这项工作中，奇异谱分析 (SSA) 和 ICA 与广义莫罗包络总变异 (GMETV) 技术相结合，从单通道 EEG 中同时去除不同伪影的组合。在这项工作中，SSA 用于分解受污染的单通道 EEG，而 ICA 用于将各种隐藏源分离为独立组件（IC）。尽管 ICA 在源分离方面是足够的，但仍有一些基本的 EEG 信号数据作为伪影出现在 IC 中。因此，消除这一点将使 EEG 信号信息丢失。本文提出了 GMETV 方法，以估计实际工件以解决这些问题。从伪影 IC 中减去估计的实际伪影，提供 EEG 所需成分的残余。该残基被加回到剩余的 IC 中，以获得去噪的 EEG。仿真结果表明，与现有技术相比，所提出的技术性能更好。相对均方根误差 (RRMSE) 降低了 12.02% 和 7。与 SSA-ICA 和 SSA-ICA 阈值相比分别为 22%。类似地，相对于 SSA-ICA 和 SSA-ICA 阈值，相关系数 (CC) 分别增加了 21.48% 和 8.25%。