Background

Electroencephalogram (EEG) signals are obtained from the scalp surface to study various neuro-physiological functions of brain. Often, these signals are obscured by the other physiological signals of the subject from heart, eye and facial muscles. Hence, the successive applications of EEG are adversely affected. The wide spectrum and high amplitude variation of muscle artifact overlaps EEG both in spectral and temporal domain.

Objective

In this paper, an adaptive singular spectrum analysis (SSA) algorithm is proposed to remove muscle artifact from single channel EEG. The mobility threshold for the SSA routine is decided adaptively using a neural network regressor (NNR). The NNR is trained using the features of contaminated EEG with various levels of contamination for better approximation of the reconstructed EEG signal.

Results

The proposed algorithm is validated using both simulated and experimental data. Parameters like relative root mean square error ($RRMSE$), correlation coefficient ($C_f$), peak signal to noise ratio ($PSNR$), and mutual information (MI) along with graphical results are used to evaluate the performance of the proposed algorithm. The proposed algorithm is found to be having consistent and better performance while the other algorithms show a decline in performance with high level of contamination.

Conclusion

The algorithm upon testing with both simulated and experimental data, is able to discriminate between various contamination levels present in EEG and performed comparatively better than the existing single channel algorithms.

中文翻译：

---

使用神经网络回归器从单通道 EEG 中去除基于自适应 SSA 的肌肉伪影

背景

从头皮表面获得脑电图 (EEG) 信号，以研究大脑的各种神经生理功能。通常，这些信号会被对象来自心脏、眼睛和面部肌肉的其他生理信号所掩盖。因此，EEG的连续应用受到不利影响。肌肉伪影的宽频谱和高幅度变化在频谱和时间域中与 EEG 重叠。

客观的

在本文中，提出了一种自适应奇异谱分析（SSA）算法来去除单通道脑电图中的肌肉伪影。SSA 例程的移动性阈值是使用神经网络回归器 (NNR) 自适应地决定的。NNR 使用具有各种污染水平的受污染 EEG 的特征进行训练，以更好地逼近重建的 EEG 信号。

结果

使用模拟和实验数据验证了所提出的算法。参数，如相对均方根误差 ($\mathrm{电阻}\mathrm{电阻}米秒乙$), 相关系数 ($C_F$), 峰值信噪比 ($磷秒N\mathrm{电阻}$)、互信息 ( MI ) 以及图形结果用于评估所提出算法的性能。发现所提出的算法具有一致且更好的性能，而其他算法在高污染水平下表现出性能下降。

结论

该算法通过模拟和实验数据进行测试，能够区分 EEG 中存在的各种污染水平，并且比现有的单通道算法表现更好。