Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review

Abstract

With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.

Learning based motion artifacts processing in fNIRS: a mini review

“…The enumeration of methods is presented in Table 1 , wherein it is observed that different fNIRS datasets were applied. Also, evaluation metrics varied across the studies, with Gao et al ( 2022 ) reporting results through visual inspection and MSE using ground truth, Lee et al ( 2017 ) and Lee et al ( 2018 ) employing CNR and Region of Interest (ROI), Siddiquee et al ( 2020 ) utilizing MA-contaminated signal classification accuracy, Kim et al ( 2022 ) reporting results using CC, AUC, and MSE, and Huang et al ( 2022a ) adopting Signal Distortion Ratio (SDR) and Normalized MSE (NMSE) for result evaluation. Consequently, due to the diversity of datasets and evaluation metrics, direct comparison of performance across these methods is not feasible.…”

Neuroimaging evidence of visual-vestibular interaction accounting for perceptual mislocalization induced by head rotation

“…Based on iteratively reweighting the temporal derivatives of the fNIRS signal, this parameter-free algorithm uses a robust regression approach to remove large fluctuations such as spikes and baseline shifts attributed to motion artifacts while leaving smaller, hemodynamic fluctuations, which can be used on either optical density or hemoglobin concentration signals. 46 , 48 – 51 …”

“…hemodynamic fluctuations, which can be used on either optical density or hemoglobin concentration signals. 46,[48][49][50][51] To confirm the validity of the TDDR method, we conducted a repeated-measures ANOVA on the signal-to-noise ratio (SNR) of ABS data with three factors (wavelength, channel, and correction: raw versus TDDR-corrected). Focusing on the factor of correction, the results showed a significant main effect [Fð1;27Þ ¼ 31.81, p < 0.001, η 2 p ¼ 0.54], with an SNR increment (ΔSNR) of 3.42 dB after the TDDR correction.…”

Mobile fNIRS for exploring inter-brain synchrony across generations and time

“…For real-world data collected while participants move freely, visual inspection of the signal is particularly important to ensure that motion artifacts are truly minimized (Pinti et al, 2018 ). Given the importance of motion correction, we refer readers to Huang et al ( 2022 ) review of algorithms for motion correction, as well as the role of accelerometers in correcting head motion, or Delgado Reyes et al ( 2018 ) comparison of algorithms implemented on children's data. Participant age is a relevant consideration when choosing a motion correction algorithm, as infants and children tend to make more frequent and larger movements than adults, meaning that certain algorithms may be more suitable than others (Di Lorenzo et al, 2019 ; Fishburn et al, 2019 ; Hu et al, 2020 ).…”