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EMD-based EEG signal enhancement for auditory evoked potential recovery under high stimulus-rate paradigm

机译:基于EMD的EEG信号增强在高刺激率范式下可用于听觉诱发电位恢复

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Short inter-stimulus interval (ISI) is one inherent characteristic of the high stimulus-rate (HSR) paradigms for studying auditory evoked potentials (AEPs). At short ISIs, the AEPs to adjacent stimuli overlap. To resolve the AEP to a specific stimulus requires an inverse process of overlapping. Inverse filtering (also called as deconvolution) has been commonly employed to achieve this goal. However, the resulted signal may be severely distorted as inverse filtering can substantially amplify such undesired components as noises and artifacts in the raw EEG recordings. In practice, even if care be taken to obtain quality EEGs, noises and artifacts are unavoidable. It is thus critical to remove or at least supress these undesired components for studies using HSR paradigms. In this paper, we propose a systematic approach to EEG signal enhancement based on empirical mode decomposition (EMD) and threshold filtering/rejection. Using synthetic and real data, we test the effectiveness of our approach. Results for both types of data consistently demonstrate that our methods can significantly improve the quality of recovered AEPs, according to visual inspection and SNRs estimated using two metrics.
机译:短的刺激间间隔(ISI)是研究听觉诱发电位(AEP)的高刺激率(HSR)范式的固有特征之一。在短ISI时,相邻刺激的AEP重叠。为了将AEP解析为特定刺激,需要反向的重叠过程。逆滤波(也称为反卷积)已普遍用于实现此目标。然而,由于逆滤波可实质上放大原始EEG记录中的诸如噪声和伪像之类的不期望成分,因此所产生的信号可能会严重失真。在实践中,即使小心获得高质量的脑电图,噪声和伪影也是不可避免的。因此,对于使用HSR范例进行研究而言,至关重要的是删除或至少抑制这些不想要的组件。在本文中,我们提出了一种基于经验模式分解(EMD)和阈值滤波/抑制的系统性EEG信号增强方法。使用综合和真实数据,我们测试了该方法的有效性。两种数据的结果一致表明,根据目测和使用两个指标估算的SNR,我们的方法可以显着提高回收的AEP的质量。

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