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首页> 外文期刊>Journal of Neuroscience Methods >FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection.
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FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection.

机译：更快：全自动统计阈值，可进行EEG伪影剔除。

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Electroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of artifacts can be attenuated by deleting data with amplitudes over a certain value, for example. Independent component analysis (ICA) separates EEG data into neural activity and artifact; once identified, artifactual components can be deleted from the data. Often, artifact rejection algorithms require supervision (e.g., training using canonical artifacts). Many artifact rejection methods are time consuming when applied to high-density EEG data. We describe FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection). Parameters were estimated for various aspects of data (e.g., channel variance) in both the EEG time series and in the independent components of the EEG: outliers were detected and removed. FASTER was tested on both simulated EEG (n=47) and real EEG (n=47) data on 128-, 64-, and 32-scalp electrode arrays. FASTER was compared to supervised artifact detection by experts and to a variant of the Statistical Control for Dense Arrays of Sensors (SCADS) method. FASTER had >90% sensitivity and specificity for detection of contaminated channels, eye movement and EMG artifacts, linear trends and white noise. FASTER generally had >60% sensitivity and specificity for detection of contaminated epochs, vs. 0.15% for SCADS. FASTER also aggregates the ERP across subject datasets, and detects outlier datasets. The variance in the ERP baseline, a measure of noise, was significantly lower for FASTER than either the supervised or SCADS methods. ERP amplitude did not differ significantly between FASTER and the supervised approach.
机译：脑电图（EEG）数据通常被伪影污染（例如，通过眼球运动）。例如，可以通过删除幅度超过某个值的数据来减弱伪影的影响。独立成分分析（ICA）将EEG数据分为神经活动和伪影;一旦确定，就可以从数据中删除人为因素。通常，伪像剔除算法需要监督（例如，使用规范伪像进行训练）。当应用于高密度EEG数据时，许多伪像剔除方法非常耗时。我们描述了更快（EEG伪影剔除的全自动统计阈值）。在EEG时间序列和EEG的独立组成部分中估计数据各个方面的参数（例如通道方差）：检测并消除异常值。在模拟的EEG（n = 47）和真实EEG（n = 47）数据上，分别在128、64和32头皮电极阵列上测试了FASTER。 FASTER与专家监督的伪影检测以及传感器密集阵列统计控件（SCADS）方法的一种变体进行了比较。 FASTER在检测受污染的通道，眼球运动和EMG伪影，线性趋势和白噪声方面具有> 90％的灵敏度和特异性。 FASTER通常具有> 60％的灵敏度和特异性来检测受污染的时期，而SCADS的灵敏度和特异性则为0.15％。 FASTER还可跨主题数据集汇总ERP，并检测异常数据集。对于FASTER，ERP基线（衡量噪声的指标）的方差明显低于监督方法或SCADS方法。 FASTER和监督方法之间的ERP幅度没有显着差异。

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