Jove
Visualize
Contact Us

Related Concept Videos

IR Spectrometers01:25

IR Spectrometers

There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...

You might also read

Related Articles

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

Sort by
Same author

A Tablet Computer-Based Food Record for the Self-Assessment of Nutritional Intake in Patients Undergoing Geriatric Rehabilitation: Prospective Pilot Feasibility Study.

JMIR aging·2026
Same author

Requirements for mHealth and Augmented Reality Apps for Patient Education Regarding Colorectal Cancer Surgery: Focus Group Study.

JMIR formative research·2026
Same author

Oscillatory and gaze signatures of socio-emotional speech processing, visuo-spatial cognition, and their interaction in a near-realistic dual-task MEG study.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Comparing Patient Simulation With a Humanoid Robot or a Human Actor in Terms of Training Success and Acceptance: Pilot Questionnaire Study.

JMIR formative research·2025
Same author

Gait Event Detection and Gait Parameter Estimation from a Single Waist-Worn IMU Sensor.

Sensors (Basel, Switzerland)·2025
Same author

Word onset tracking in neural responses of human basal ganglia nuclei.

Brain structure & function·2025
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 Experiment Video

Updated: Jun 16, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

30.5K

Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement

Hayder R Al-Omairi1,2, Arkan Al-Zubaidi1,3, Sebastian Fudickar4,5

  • 1Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
Summary

This study introduces a new nonlinear Hammerstein-Wiener model to reduce motion artifacts in functional near-infrared spectroscopy (fNIRS) brain activity measurements. The method effectively removes movement interferences, improving signal quality for robust analysis.

Keywords:
NIRS signal improvementaccelerometerfunctional near-infrared spectroscopy fNIRSgyroscopemotion artifactmotion correctionmulti-channel IMU

More Related Videos

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

3.1K
Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

486

Related Experiment Videos

Last Updated: Jun 16, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

30.5K
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

3.1K
Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

486

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Participant movement introduces significant artifacts in functional near-infrared spectroscopy (fNIRS) data.
  • Motion artifacts (MAs) compromise the accuracy of brain activity estimations in fNIRS.
  • Effective mitigation of MAs is essential for reliable fNIRS research.

Purpose of the Study:

  • To propose and evaluate a novel nonlinear Hammerstein-Wiener model for estimating and mitigating motion artifacts in fNIRS signals.
  • To compare the performance of the proposed method against established techniques using motion data from IMU sensors.

Main Methods:

  • Applied a nonlinear Hammerstein-Wiener model to fNIRS signals, utilizing data from head-mounted and probe-mounted inertial measurement unit (IMU) sensors.
  • Analyzed hemodynamic responses (oxyhemoglobin and deoxyhemoglobin) in 17 participants performing a hand-tapping task with varying head movements.
  • Compared the novel approach with eight existing methods (PCA, tPCA, spline, etc.) using SNR, △AUC, RMSE, and R metrics.

Main Results:

  • The nonlinear Hammerstein-Wiener method demonstrated a statistically significant increase in signal-to-noise ratio (SNR) (p < 0.001) compared to all other methods.
  • Visual analysis confirmed superior mitigation of motion artifact contamination by the proposed approach.
  • The MA correction quality achieved by the new method was comparable to that obtained using head-IMU and probe-IMU data directly.

Conclusions:

  • The nonlinear Hammerstein-Wiener model offers a highly effective solution for mitigating motion artifacts in fNIRS data.
  • This novel approach enhances the robustness of brain activity estimations derived from fNIRS.
  • The method provides a valuable tool for improving the quality and reliability of fNIRS studies.