在功能近红外光谱(fNIRS)实验中,参与者的运动是伪影的主要来源。减轻运动伪影(MA)的影响对于可靠地估计大脑活动至关重要。这里,我们建议并评估了非线性Hammerstein-Wiener模型的一种新颖应用,以通过安装在参与者头部(head-IMU)和fNIRS探头(probe-IMU)上的IMU传感器进行直接运动记录来估计和减轻fNIRS信号中的MA。为此,我们分析了17名参与者的单通道氧合血红蛋白(HbO)和脱氧血红蛋白(HbR)信号的血流动力学反应,这些参与者进行了不同水平的头部同时运动的手敲击任务.此外,敲击任务是在没有头部运动的情况下执行的,以估计地面实况大脑的激活。我们将探针IMU和头部IMU的新颖方法的性能与八种已建立的方法进行了比较(PCA,tPCA,样条,样条Savitzky-Golay,小波,CBSI,RLOESS,和WCBSI)在四个质量度量上:SNR,△AUC,RMSE,和R。我们提出的非线性Hammerstein-Wiener方法在所有方法中都获得了最佳的SNR增长(p<0.001)。目视检查显示,我们的方法减轻了其他技术无法有效去除的MA污染。MA校正质量与头部和探头IMU相当。
Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant\'s head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky-Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein-Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs.