Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data

Abstract

Motion artifacts are a significant source of noise in many functional near-infrared spectroscopy (fNIRS) experiments. Despite this, there is no well-established method for their removal. Instead, functional trials of fNIRS data containing a motion artifact are often rejected completely. However, in most experimental circumstances the number of trials is limited, and multiple motion artifacts are common, particularly in challenging populations. Many methods have been proposed recently to correct for motion artifacts, including principle component analysis, spline interpolation, Kalman filtering, wavelet filtering and correlation-based signal improvement. The performance of different techniques has been often compared in simulations, but only rarely has it been assessed on real functional data. Here, we compare the performance of these motion correction techniques on real functional data acquired during a cognitive task, which required the participant to speak aloud, leading to a low-frequency, low-amplitude motion artifact that is correlated with the hemodynamic response. To compare the efficacy of these methods, objective metrics related to the physiology of the hemodynamic response have been derived. Our results show that it is always better to correct for motion artifacts than reject trials, and that wavelet filtering is the most effective approach to correcting this type of artifact, reducing the area under the curve where the artifact is present in 93% of the cases. Our results therefore support previous studies that have shown wavelet filtering to be the most promising and powerful technique for the correction of motion artifacts in fNIRS data. The analyses performed here can serve as a guide for others to objectively test the impact of different motion correction algorithms and therefore select the most appropriate for the analysis of their own fNIRS experiment.

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

“…In fact, head movements can cause probe displacements with consequent motion artifacts that corrupt the optical identification of brain activity 36 . Moreover, optical sensors are sensitive to stray light (e.g., sunlight when experiments are performed outside), creating additional noise in fNIRS signals.…”

Quantitative and qualitative discrimination of task periods from non-task periods by recurrence plots

“…Second, OP L differs between measuring points, near-infrared wavelengths, and subjects. NIRS data contain physiological noise (cardiac circulation, respiration [2], and scalp blood flow [4,5]), motion artifacts [6], and mechanical noise. An example of NIRS data is shown in Fig.…”

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy

“…In particular, the two major sources of confounding noise that affect the analysis and interpretation of fNIRS signals are serially correlated errors due to systemic physiology, such as cardiac, respiratory, and low-frequency Mayer waves (related to blood pressure regulation), and motion artifacts due to the movement or slippage of the head cap. While several approaches to offline correction of motion [25][26][27][28] and physiological noise [29][30][31] have been proposed, for real-time imaging, these corrections must be both automated and quickly implemented to keep up with the high sample rates of fNIRS systems. In addition, real-time correction methods need to be forward directed, meaning that they need to offer corrections using only past history data points.…”

“…In addition, real-time correction methods need to be forward directed, meaning that they need to offer corrections using only past history data points. In contrast, many of the motion correction methods, such as wavelet or spline interpolation models, 26,28 use data information from both before and after the artifact for correction and thus only offer retrospective corrections.…”