As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease groups, motion artifacts in the NIRS signal due to subject movement is becoming an important challenge. Motion artifacts generally produce signal fluctuations that are larger than physiological NIRS signals, thus it is crucial to correct for them before obtaining an estimate of stimulus evoked hemodynamic responses. There are various methods for correction such as principle component analysis (PCA), wavelet-based filtering and spline interpolation. Here, we introduce a new approach to motion artifact correction, targeted principle component analysis (tPCA), which incorporates a PCA filter only on the segments of data identified as motion artifacts. It is expected that this will overcome the issues of filtering desired signals that plagues standard PCA filtering of entire data sets. We compared the new approach with the most effective motion artifact correction algorithms on a set of data acquired simultaneously with a collodion-fixed probe (low motion artifact content) and a standard Velcro probe (high motion artifact content). Our results show that tPCA gives statistically better results in recovering hemodynamic response function (HRF) as compared to wavelet-based filtering and spline interpolation for the Velcro probe. It results in a significant reduction in mean-squared error (MSE) and significant enhancement in Pearson's correlation coefficient to the true HRF. The collodion-fixed fiber probe with no motion correction performed better than the Velcro probe corrected for motion artifacts in terms of MSE and Pearson's correlation coefficient. Thus, if the experimental study permits, the use of a collodion-fixed fiber probe may be desirable. If the use of a collodion-fixed probe is not feasible, then we suggest the use of tPCA in the processing of motion artifact contaminated data.

中文翻译：

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靶向主成分分析：一种新的近红外光谱运动伪影校正方法

随着近红外光谱 (NIRS) 将其应用范围扩大到不同年龄和疾病组，由于对象运动导致的 NIRS 信号中的运动伪影正成为一项重要挑战。运动伪影通常会产生大于生理 NIRS 信号的信号波动，因此在获得对刺激诱发的血流动力学反应的估计之前对其进行校正至关重要。有多种校正方法，例如主成分分析 (PCA)、基于小波的滤波和样条插值。在这里，我们介绍了一种新的运动伪影校正方法，即目标主成分分析 (tPCA)，它仅在识别为运动伪影的数据段上结合了 PCA 滤波器。预计这将克服困扰整个数据集的标准 PCA 过滤的所需信号的过滤问题。我们将新方法与最有效的运动伪影校正算法对一组数据进行了比较，该数据集同时使用火棉胶固定探针（低运动伪影内容）和标准 Velcro 探针（高运动伪影内容）。我们的结果表明，与 Velcro 探针的基于小波的滤波和样条插值相比，tPCA 在恢复血流动力学响应函数 (HRF) 方面提供了更好的统计结果。它导致均方误差 (MSE) 显着降低，皮尔逊相关系数与真实 HRF 的相关系数显着增强。在 MSE 和 Pearson 相关系数方面，没有运动校正的胶棉固定光纤探头的性能优于校正运动伪影的 Velcro 探头。因此，如果实验研究允许，使用火棉胶固定光纤探头可能是可取的。如果使用火棉胶固定探头不可行，那么我们建议在处理运动伪影污染数据时使用 tPCA。