How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation

By Felix Scholkmann, S. Spichtig, Thomas Muehlemann, Martin Wolf. Physiological Measurement, 2010

662 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2e13c899bfbb3e5109a2bc2747fc5219adc08988

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions (2020, methodology): “Using different methods, similar conclusions were drawn by Shinoura and Yamada (2005).”
Wavelet-based motion artifact removal for functional near-infrared spectroscopy (2012, methodology): “Scholkmann et al (2010) used a moving standard deviation scheme to detect motion artifacts and applied spline interpolation in the time interval of the motion to model the artifact and subtract it from the signal.”
Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS (2018, neutral): “Other methods such as the Movement Artifact Reduction Algorithm (MARA) (Scholkmann et al., 2010) and Targeted Principal Components Analysis (tPCA) (Yücel et al.”
Statistical analysis of fNIRS data: A comprehensive review (2014, neutral): “…compared the performance of the four motion correction methods, including Kalman filtering (Izzetoglu et al., 2010), wavelet-based filtering (Molavi and Dumont, 2012), spline interpolation (Scholkmann et al., 2010), and principal component analysis (Wilcox et al., 2008, 2010; Zhang et al., 2005).”
A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology (2014, background): “These are mostly due to changes in light coupling to the tissue, but part could also be due to genuine changes in CBF/CBV, in the fNIRI signal (Izzetoglu et al., 2005; Kurihara et al., 2003; Özgören et al., 2012; Scholkmann et al., 2010; Takahashi et al., 2012; Tax et al., 2011).”
Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data (2014, methodology): “, 2012; Molavi and Dumont, 2012), other techniques can perform better for particular fNIRS datasets (Izzetoglu et al., 2010; Scholkmann et al., 2010).”
A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy (2012, methodology): “In cases where real motion artifact contaminated NIRS data has been used, the utility of a correction technique has been quantified using some measure of improvement in signal-to-noise ratio of the corrected NIRS timecourse (Izzetoglu et al., 2010; Scholkmann et al., 2010; Molavi and Dumont, 2012).”