Statistical control of artifacts in dense array EEG/MEG studies
Statistical control of artifacts in dense array EEG/MEG studies
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DOI：
10.1017/s0048577200980624
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发表时间：
2000-07-01
期刊：
影响因子：
3.7
通讯作者：
Rockstroh, B
中科院分区：
文献类型：
--
作者：
Junghöfer, M;Elbert, T;Rockstroh, B
随着脑电图和脑磁图研究中密集传感器阵列（64-256通道）的出现，一些记录通道被伪影污染的可能性增加。如果要求所有通道都没有伪影，则可接受的试验次数可能低得令人无法接受。精确的伪影筛选对于精确的空间映射、电流密度测量、源分析和基于单次试验方法的精确时间分析是必要的。考虑到大的数据集，精确的筛选带来了许多问题。我们提出了密集阵列研究（SCADS）中伪信号的统计校正程序，该程序(1)使用记录参考检测单个通道伪信号，(2)使用平均参考检测全局伪信号，(3)在所有传感器的基础上用统计加权的球面插值替换伪信号污染的传感器，以及(4)计算各试验信号的方差以记录平均波形的稳定性。来自128通道记录和数值模拟的例子说明了在避免分析错误中仔细审查工件的重要性。
With the advent of dense sensor arrays (64-256 channels) in electroencephalography and magnetoencephalography studies, the probability increases that some recording channels are contaminated by artifact. If all channels are required to be artifact free, the number of acceptable trials may be unacceptably low. Precise artifact screening is necessary for accurate spatial mapping, for current density measures, for source analysis, and for accurate temporal analysis based on single-trial methods. Precise screening presents a number of problems given the large datasets. We propose a procedure for statistical correction of artifacts in dense array studies (SCADS), which (1) detects individual channel artifacts using the recording reference, (2) detects global artifacts using the average reference, (3) replaces artifact-contaminated sensors with spherical interpolation statistically weighted on the basis of all sensors, and (4) computes the variance of the signal across trials to document the stability of the averaged waveform. Examples from 128-channel recordings and from numerical simulations illustrate the importance of careful artifact review in the avoidance of analysis errors.