Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky–Golay filtering

Abstract

Motion artifact contamination in near-infrared spectroscopy (NIRS) data has become an important challenge in realizing the full potential of NIRS for real-life applications. Various motion correction algorithms have been used to alleviate the effect of motion artifacts on the estimation of the hemodynamic response function. While smoothing methods, such as wavelet filtering, are excellent in removing motion-induced sharp spikes, the baseline shifts in the signal remain after this type of filtering. Methods, such as spline interpolation, on the other hand, can properly correct baseline shifts; however, they leave residual high-frequency spikes. We propose a hybrid method that takes advantage of different correction algorithms. This method first identifies the baseline shifts and corrects them using a spline interpolation method or targeted principal component analysis. The remaining spikes, on the other hand, are corrected by smoothing methods: Savitzky-Golay (SG) filtering or robust locally weighted regression and smoothing. We have compared our new approach with the existing correction algorithms in terms of hemodynamic response function estimation using the following metrics: mean-squared error, peak-to-peak error ([Formula: see text]), Pearson's correlation ([Formula: see text]), and the area under the receiver operator characteristic curve. We found that spline-SG hybrid method provides reasonable improvements in all these metrics with a relatively short computational time. The dataset and the code used in this study are made available online for the use of all interested researchers.

Global Motion Detection and Censoring in High-Density Diffuse Optical Tomography

“…GVTD could be used as an alternative to either absolute signal amplitudes or windowed amplitude changes included in the Homer2 code package [62]. Further, GVTD could be used in conjunction with motion correction methods such as spline interpolation (MARA) [34], Kalman filtering [58,63], PCA [58], tPCA [46], Hybrid methods [36], or any method that depends on motion detection in the temporal domain. However, we note that, in the results presented here, GVTD-based censoring alone provided better image quality than any of the alternative motion correction procedures.…”

“…The spline and Savitzky-Golay hybrid method is a three-step algorithm that aims to identify and correct different types of motion artifact [36]. First, single-channel measurements are passed through a Sobel filter to identify time-points exceeding a threshold of 1.5 times the interquartile interval of the signal gradient.…”

“…After this step, the hybrid method then applies a Savitsky-Golay smoothing filter to remove the remaining fast spikes. We used the "hmrMotionCorrectSplineSG" function defined in the original paper with its default parameters and setting p = .99 and FrameSize_sec = 1.5 [36] (Tables 1S and 2S, Fig. S1).…”

“…Most fNIRS studies measure the efficiency of motion removal techniques based on the recovery of a synthetic HRF [31][32][33]36], or, in the case of real data, based on the variance across subjects or datasets [33]. However, since HD-DOT is focused on creating images comparable to fMRI, throughout this paper, we have used an fMRI dataset with the same task and resting state paradigm used in our HD-DOT datasets as a gold standard for evaluating the efficacy of different motion removal methods.…”

“…However, a consensus on how best to correct for motion artifacts has not emerged [31][32][33]. Extant motion correction methods in fNIRS largely involve two steps: first, motion detection, and second, signal correction [34][35][36][37][38][39]. The fNIRS literature has largely focused on correcting motion artifacts on individual source-detector pair measurements and much less attention has been placed on multichannel or full arrays assessments.…”