Motion Artifact Removal for Functional Near Infrared Spectroscopy: A Comparison of Methods
Motion Artifact Removal for Functional Near Infrared Spectroscopy: A Comparison of Methods
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DOI：
10.1109/tbme.2009.2038667
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发表时间：
2010-06-01
影响因子：
4.6
通讯作者：
Meintjes, E. M.
中科院分区：
文献类型：
--
作者：
Robertson, F. C.;Douglas, T. S.;Meintjes, E. M.
近红外光谱 (NIRS) 在功能性脑成像领域迅速普及。它非常适合对患者或儿童的研究；然而，特别是在这些人群中，运动伪影可能会带来问题。在这里，我们建议使用光源和探测器之间距离可忽略不计的成像通道来检测对象运动，而不需要额外的运动传感器。包含故意运动伪影的数据集是从三个受试者那里获得的。可以在来自同位通道的信号中检测到运动伪影，最小灵敏度为 0.75，特异性为 0.98。比较了从功能信号中消除运动伪影的五种技术，即双输入递归最小二乘法 (RLS) 自适应滤波、基于小波的滤波、独立分量分析 (ICA) 以及两通道和多通道回归。在大多数数据集中，使用 ICA 或多通道回归时，所有通道的 SNR 中值变化最大。 RLS 自适应滤波产生的 SNR 增幅最小。在存在尖锐尖峰的地方，小波滤波产生最大的信噪比增加。 ICA 和多通道回归是减少功能 NIRS 中运动伪影的有前景的方法，且无需耗时的手动技术。
Near infrared spectroscopy (NIRS) is rapidly gaining popularity for functional brain imaging. It is well suited to studies of patients or children; however, in these populations particularly, motion artifacts can present a problem. Here, we propose the use of imaging channels with negligible distance between light source and detector to detect subject motion, without the need for an additional motion sensor. Datasets containing deliberate motion artifacts were obtained from three subjects. Motion artifacts could be detected in the signal from the co-located channels with a minimum sensitivity of 0.75 and specificity of 0.98. Five techniques for removing motion artifact from the functional signals were compared, namely two-input recursive least squares (RLS) adaptive filtering, wavelet-based filtering, independent component analysis (ICA), and two-channel and multiple-channel regression. In most datasets, the median change in SNR across all channels was the greatest using ICA or multiple-channel regression. RLS adaptive filtering produced the smallest increase in SNR. Where sharp spikes were present, wavelet filtering produced the largest SNR increase. ICA and multiple-channel regression are promising ways to reduce motion artifact in functional NIRS without requiring time-consuming manual techniques.