Zapline-plus: A Zapline extension for automatic and adaptive removal of frequency-specific noise artifacts in M/EEG.
Zapline-plus: A Zapline extension for automatic and adaptive removal of frequency-specific noise artifacts in M/EEG.
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DOI：
10.1002/hbm.25832
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发表时间：
2022-06-15
影响因子：
4.8
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中科院分区：
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从电生理数据中去除电源线噪声和其他频率特定伪影而不影响神经信号仍然是一项具有挑战性的任务。最近，引入了一种结合光谱和空间滤波来有效去除线噪声的方法：Zapline。然而，该算法需要手动选择噪声频率和在空间滤波期间要去除的空间分量的数量。此外，它假设噪声频率和空间地形随时间推移是稳定的，这往往是不必要的。为了克服这些问题，我们引入了Zapline‐plus，它允许自适应和自动去除M/脑电图（EEG）和LFP数据中的频率特定噪声伪影。为了实现这一点，我们的扩展首先将数据分割成噪声在空间上稳定的周期（块）。然后，对于每个块，它搜索功率谱中的峰值，最后应用Zapline。还针对每个块单独确定所找到的目标频率周围的确切噪声频率，以允许峰值噪声频率随时间的波动。Zapline的待移除组件数量使用离群值检测算法自动确定。最后，分析清洁后的频谱，以确定是否存在次优清洁，并在重新运行工艺前根据需要调整参数。该软件创建了一个详细的图监测清洁。我们通过将其应用于四个公开可用的数据集、两个包含固定和移动的任务条件的EEG集以及两个包含强线噪声的脑磁图集，强调了我们算法不同功能的功效。Zapline‐plus是Zapline的一个包装器，它允许完全自动和自适应地去除特定频率的伪影（线和其他），同时确保对其他频率的影响最小。它还可以创建全面的分析图，以便了解过程并在必要时进行进一步调整。
Removing power line noise and other frequency‐specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral and spatial filtering to effectively remove line noise: Zapline. This algorithm, however, requires manual selection of the noise frequency and the number of spatial components to remove during spatial filtering. Moreover, it assumes that noise frequency and spatial topography are stable over time, which is often not warranted. To overcome these issues, we introduce Zapline‐plus, which allows adaptive and automatic removal of frequency‐specific noise artifacts from M/electroencephalography (EEG) and LFP data. To achieve this, our extension first segments the data into periods (chunks) in which the noise is spatially stable. Then, for each chunk, it searches for peaks in the power spectrum, and finally applies Zapline. The exact noise frequency around the found target frequency is also determined separately for every chunk to allow fluctuations of the peak noise frequency over time. The number of to‐be‐removed components by Zapline is automatically determined using an outlier detection algorithm. Finally, the frequency spectrum after cleaning is analyzed for suboptimal cleaning, and parameters are adapted accordingly if necessary before re‐running the process. The software creates a detailed plot for monitoring the cleaning. We highlight the efficacy of the different features of our algorithm by applying it to four openly available data sets, two EEG sets containing both stationary and mobile task conditions, and two magnetoencephalography sets containing strong line noise. Zapline‐plus is a wrapper for Zapline which allows fully automatic and adaptive removal of frequency‐specific artifacts (line and others) while ensuring minimal impact on other frequencies. It also creates comprehensive analysis plots that allow understanding the process and further adaptation if necessary.
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