Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes.
Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes.
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DOI：
10.1152/jn.00315.2021
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发表时间：
2021-11-01
影响因子：
2.5
通讯作者：
Woolrich MW
中科院分区：
文献类型：
--
作者：
Fabus MS;Quinn AJ;Warnaby CE;Woolrich MW
神经生理信号通常是有噪声的、非正弦的，并且由瞬态突发组成。在此类数据集中提取和分析振荡特征（例如波形形状和交叉频率耦合）仍然很困难。这限制了我们对大脑动力学及其功能重要性的理解。在这里，我们开发了迭代掩蔽经验模式分解（itEMD），一种旨在以灵活、完全数据驱动的方式将噪声和瞬态单通道数据分解为相关振荡模式的方法，无需手动调整。该技术基于经验模态分解（EMD），可以通过相位对齐的瞬时频率提取单周期波形动态。我们通过在不同的噪声、稀疏性和非正弦性条件下进行广泛的模拟来测试我们的方法。我们发现 itEMD 显着改进了将数据分离为不同的非正弦振荡分量，并在各种相关参数中稳健地再现波形形状。我们进一步验证了多模式、多物种电生理数据的技术。我们的 itEMD 提取已知的大鼠海马 θ 波形不对称性，并识别特定于受试者的人类枕骨 α，而无需事先假设信号中包含的频率。值得注意的是，与现有的基于 EMD 的方法相比，它的模式混合显着减少。通过减少模式混合并简化 EMD 结果的解释，itEMD 将能够对神经信号在行为和疾病中的功能作用进行新的分析。新颖且值得注意的我们引入了一种新颖的数据驱动方法来识别神经记录中的振荡。这种方法基于经验模式分解，减少了组件的混合，这是其主要问题之一。使用模拟和真实数据对该技术进行了验证并与现有方法进行了比较。我们展示了我们的方法可以更好地提取高噪声和非正弦数据集中的振荡及其属性。
Neurophysiological signals are often noisy, nonsinusoidal, and consist of transient bursts. Extraction and analysis of oscillatory features (such as waveform shape and cross-frequency coupling) in such data sets remains difficult. This limits our understanding of brain dynamics and its functional importance. Here, we develop iterated masking empirical mode decomposition (itEMD), a method designed to decompose noisy and transient single-channel data into relevant oscillatory modes in a flexible, fully data-driven way without the need for manual tuning. Based on empirical mode decomposition (EMD), this technique can extract single-cycle waveform dynamics through phase-aligned instantaneous frequency. We test our method by extensive simulations across different noise, sparsity, and nonsinusoidality conditions. We find itEMD significantly improves the separation of data into distinct nonsinusoidal oscillatory components and robustly reproduces waveform shape across a wide range of relevant parameters. We further validate the technique on multimodal, multispecies electrophysiological data. Our itEMD extracts known rat hippocampal θ waveform asymmetry and identifies subject-specific human occipital α without any prior assumptions about the frequencies contained in the signal. Notably, it does so with significantly less mode mixing compared with existing EMD-based methods. By reducing mode mixing and simplifying interpretation of EMD results, itEMD will enable new analyses into functional roles of neural signals in behavior and disease. NEW & NOTEWORTHY We introduce a novel, data-driven method to identify oscillations in neural recordings. This approach is based on empirical mode decomposition and reduces mixing of components, one of its main problems. The technique is validated and compared with existing methods using simulations and real data. We show our method better extracts oscillations and their properties in highly noisy and nonsinusoidal datasets.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
3.7
作者：
Gerber EM;Sadeh B;Ward A;Knight RT;Deouell LY
通讯作者：
Deouell LY
DOI：
10.1073/pnas.1517629112
发表时间：
2015-11-03
影响因子：
11.1
作者：
Feingold, Joseph;Gibson, Daniel J.;Graybiel, Ann M.
通讯作者：
Graybiel, Ann M.
影响因子：
20.6
作者：
BOASHASH, B
通讯作者：
BOASHASH, B
影响因子：
3
作者：
Klimesch, W
通讯作者：
Klimesch, W