功能性近红外光谱技术(fNIRS)作为一种新兴的神经成像技术得到了广泛关注,然而fNIRS信号中运动伪迹的存在会使信号的处理结果产生偏差。提出了一种定向中值滤波和数学形态学相结合的算法——tMedMor算法,并采用该算法对fNIRS信号中的三种运动伪迹(包括尖峰、基线突变和缓慢漂移)进行去除;然后用仿真数据和实验数据进行了验证,并将所提算法与常用的几种算法进行对比,结果表明:tMedMor算法在均方误差、信噪比、皮尔逊相关系数的平方、峰峰误差方面具有良好的表现,说明该算法可以作为一种新方法用于fNIRS信号的预处理阶段。

Functional near-infrared spectroscopy (fNIRS) has attracted widespread attention as an emerging neuroimaging technology. However, the existence of motion artifacts in the fNIRS signal leads to bias in its signal processing outcomes. We proposed a tMedMor algorithm that combines the targeted median filtering (tMed) and mathematical morphology (Mor) for the removal of three motion artifacts in the fNIRS signal, namely, spike, baseline shift, and slow drift. Simulated and experimental data were used for verification, and the performance of the proposed algorithm was compared with those of several other common algorithms. Our results revealed that the tMedMor algorithm demonstrates good performance in terms of mean square error, signal-to-noise ratio, square of Pearson correlation coefficient, and peak-to-peak error, which together indicate that tMedMor can be applied as a new approach to the fNIRS signal at the preprocessing stage.