Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bactericidal Action and <i>in Vitro</i> Immunomodulatory Effects of Ozone on Methicillin-Resistant <i>Staphylococcus Aureus</i>.

Frontiers in bioscience (Elite edition)·2026
Same author

EXPRESS: Quantitative functional BOLD (qfBOLD): A Combined Gradient-Echo and Spin-Echo Framework for Oxygen Extraction Fraction (OEF) Mapping with functional MRI.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Ultrafast fMRI Detects Age-Related Changes in Harmonics of Cardiac Pulsations in the Brain at 7 T.

Magnetic resonance in medicine·2026
Same author

How to Improve the Reliability of Aperiodic Parameter Estimates in M/EEG: A Method Comparison.

Psychophysiology·2026
Same author

Changes in aperiodic (1/<i>f</i> slope) activity during a picture-word interference task: Effects of congruency and sequence manipulations.

bioRxiv : the preprint server for biology·2026
Same author

Region-specific associations between cerebral arterial elasticity and grey matter volume: Evidence for lateral prefrontal vulnerability.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Apr 16, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.7K

A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data.

Antonio M Chiarelli1, Edward L Maclin1, Monica Fabiani2

  • 1Beckman Institute, University of Illinois at Urbana Champaign, USA.

Neuroimage
|March 10, 2015
PubMed
Summary

A new kurtosis-based Wavelet Filtering (kbWF) method improves motion artifact correction in functional Near-Infrared Spectroscopy (fNIRS) data. kbWF enhances signal-to-noise ratio (SNR) by effectively removing transient noise, outperforming existing techniques.

Keywords:
Functional Near-Infrared Spectroscopy (fNIRS)KurtosisMotion artifactsWavelet filtering

More Related Videos

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

7.7K
Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
06:42

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Published on: January 19, 2019

11.3K

Related Experiment Videos

Last Updated: Apr 16, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.7K
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

7.7K
Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
06:42

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Published on: January 19, 2019

11.3K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Motion artifacts are a significant challenge in functional Near-Infrared Spectroscopy (fNIRS) data acquisition.
  • Existing motion correction algorithms like Principal Component Analysis (PCA) and Wavelet Filtering (WF) have limitations, particularly in high signal-to-noise ratio (SNR) conditions where they can reduce signal amplitude.

Purpose of the Study:

  • To introduce and evaluate a novel motion artifact correction method for fNIRS data, termed kurtosis-based Wavelet Filtering (kbWF).
  • To compare the effectiveness of kbWF against existing methods in preserving signal quality and removing transient noise.

Main Methods:

  • Developed kbWF, an iterative procedure that identifies and removes outlier wavelets based on the fourth moment (kurtosis) of the coefficient distribution.
  • Tested kbWF using simulated functional hemodynamic responses superimposed on real resting-state fNIRS recordings.
  • Compared kbWF performance against established methods like Principal Component Analysis (PCA) and standard Wavelet Filtering (WF).

Main Results:

  • kbWF demonstrated high effectiveness in eliminating transient noise artifacts from fNIRS data.
  • The method yielded superior signal-to-noise ratio (SNR) compared to existing techniques across various signal and noise amplitudes.
  • Kurtosis proved to be a more sensitive metric than variance for identifying outlier wavelets, contributing to kbWF's improved performance.

Conclusions:

  • Kurtosis-based Wavelet Filtering (kbWF) offers a significant advancement in correcting motion artifacts in fNIRS.
  • kbWF enhances data quality by preserving signal amplitude and improving SNR, especially in the presence of transient noise.
  • The method's iterative nature and use of kurtosis make it robust, though it does not address slow, long-duration artifacts.