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首页> 外文期刊>Biomedical signal processing and control >Two-stage wavelet shrinkage and EEG-EOG signal contamination model to realize quantitative validations for the artifact removal from multiresource biosignals
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Two-stage wavelet shrinkage and EEG-EOG signal contamination model to realize quantitative validations for the artifact removal from multiresource biosignals

机译：两阶段小波收缩和EEG-EOG信号污染模型可实现对多资源生物信号中去除伪影的定量验证

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Electroencephalogram (EEG) data inevitably contain large amounts of noise, particularly from ocular potentials in tasks with eye movements, known as electro-oculography (EOG) artifact, which has been a crucial issue in brain-computer-interface studies. This time-frequency characteristic has been substantially dealt with in previous proposed denoising algorithms that relied on a consistent assumption based on the single-noise component model. However, the traditional model is not applicable for biomedical signals that consist of multiple signal components, such as weak EEG signals that are easily recognized as noise because of the signal amplitude with respect to the EOG signal. In consideration of the realistic signal contamination, we designed a novel EEG-EOG signal contamination model to quantitatively validate artifact removal from EEGs. We then proposed the two-stage wavelet shrinkage method with the decomposition of the undecimated wavelet transform (UDWT), which is suitable for signal structure. Open-source clinical intracranial EEGs with the hundred dataset in each behavioral condition were introduced to the validation as "true EEG" before the contamination of artificial EOGs. The quality of the reconstructed EEG signal has evaluated in the frequency spectrum, which represents how much the original specific brain-state characteristic can be reconstructed. Numerical analyses demonstrated that the first stage pursued abrupt changes with high amplitudes provided by assumed EOGs, and the second stage provided the EEG frequency spectrum as observed in the original signal. What the performance exceeded the conventional shrinkage suggests that threshold values is required to be set properly depending on individual amplitudes of contaminated bio-signal sources as our proposed method demonstrated. This result focused on the actual amplitude-frequency structure in the polygenetic signal. It not only provided the decomposition performance, but also revealed how bio-signals are mixed together by using a new standard model for robust validation in the EEG-EOG signal contamination. (C) 2018 The Author(s). Published by Elsevier Ltd.
机译：脑电图（EEG）数据不可避免地包含大量噪声，特别是来自眼动任务中的眼电位的噪声，称为眼电图（EOG）伪影，这在脑机接口研究中一直是至关重要的问题。在先前提出的降噪算法中已经充分处理了这种时频特性，该算法基于基于单噪声分量模型的一致假设。但是，传统模型不适用于由多个信号分量组成的生物医学信号，例如由于相对于EOG信号的信号幅度而易于识别为噪声的弱EEG信号。考虑到实际的信号污染，我们设计了一种新颖的EEG-EOG信号污染模型，以定量验证从EEG中去除的伪影。然后我们提出了两阶段小波收缩方法，将未抽取的小波变换（UDWT）分解，该方法适用于信号结构。在污染人工EOG之前，将具有每种行为状况的一百个数据集的开源临床颅内脑电图作为“真实EEG”引入验证。已在频谱中评估了重建的EEG信号的质量，该质量代表了可以重建多少原始的特定大脑状态特征。数值分析表明，第一阶段追求由假定的EOG提供的高幅度突变，而第二阶段则提供原始信号中观察到的EEG频谱。性能超出了常规收缩率，这表明需要根据被污染的生物信号源的各个幅度正确设置阈值，如我们提出的方法所示。该结果集中于多基因信号中的实际幅度-频率结构。它不仅提供了分解性能，而且还揭示了如何通过使用新的标准模型对生物信号进行混合，以在EEG-EOG信号污染中进行可靠的验证。 （C）2018作者。由Elsevier Ltd.发布

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