Improving the accuracy of the method for removing motion artifacts from fNIRS data using ICA and an accelerometer
Improving the accuracy of the method for removing motion artifacts from fNIRS data using ICA and an accelerometer
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DOI：
10.1109/wac.2014.6935730
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
U. Yamamoto;Y. Nakamura;Hisatake Yokouchi;T. Hiroyasu
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文献类型：
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作者：
U. Yamamoto;Y. Nakamura;Hisatake Yokouchi;T. Hiroyasu
独立成分分析（伊卡）是从功能近红外光谱（fNIRS）数据中去除运动伪影的最佳方法之一。在该方法中，通过伊卡将fNIRS信号分离成分量，并且确定在fNIRS信号和运动伪影之间显示出高度相关性的分量。该分量被去除，并且得到没有运动伪影的fNIRS信号。然而，与加速度计数据相比，fNIRS数据通常在时间上延迟，因为受试者头部移动后血流变化缓慢。在使用伊卡方法时，为了消除运动伪影，需要考虑fNIRS数据中的时间延迟。在该方法中，相关系数用于识别运动伪影分量。然而，由于生物信号的微小波动，脑血流量有很小的变化。因此，相关性降低，并且难以确定该分量是否是从运动伪影导出的。我们提出了一种方法，使用t检验和相关系数来识别运动伪影。在该方法中，我们使用t检验来比较加速度计数据和伊卡分离的信号。与加速度计数据没有显著差异的分离信号被识别为运动伪影并被去除。为了检验这种方法的有效性，我们使用了包括由困倦引起的运动伪影的数据集。将仅使用相关系数获得的结果与使用相关系数和t检验获得的结果进行比较。我们发现该方法提高了去除运动伪影的准确性。此外，倒置加速度计数据的符号，并进行t检验。从而提高了去除运动伪影的准确性。
Independent component analysis (ICA) is one of the most preferred methods for removing motion artifacts from functional near-infrared spectroscopy (fNIRS) data. In this method, the fNIRS signal is separated into components by ICA and the component that shows high correlation between the fNIRS signal and motion artifact is determined. This component is removed, and the fNIRS signal without motion artifacts is derived. However, fNIRS data are often delayed temporally compared with accelerometer data because the blood flow changes slowly after the subject's head moves. It is necessary to consider the temporal delay in fNIRS data in order to remove motion artifacts when we use ICA method. In this method, the correlation coefficient is used to identify the motion artifact component. However, the cerebral blood flow has a small change because the biological signal fluctuates minutely. Hence, the correlation is reduced, and it is difficult to determine whether the component has been derived from the motion artifact. We propose a method that uses t-tests and the correlation coefficient to identify the motion artifact. In this proposed method, we used t-tests for comparing accelerometer data and signals separated by ICA. The separated signal with no significant difference from accelerometer data were identified as motion artifacts and removed. To examine the validity of this method, we used data sets including motion artifacts caused by sleepiness. Results obtained using only the correlation coefficient were compared with those obtained using the correlation coefficient and t-tests. We found that the proposed method improved that accuracy of removing motion artifacts. In addition, the signs of the accelerometer data were inverted, and t-tests were performed. Consequently, the accuracy of removing the motion artifact was improved.