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

Environmental and family correlates of daily TV-watching time in children with autism spectrum disorder and typically developing children.

Frontiers in pediatrics·2026
Same author

Functional near-infrared spectroscopy (fNIRS) biomarkers of tinnitus severity within tinnitus subtypes.

Hearing research·2026
Same author

Correction of connectivity induced by autocorrelation and filtering in resting state functional near-infrared spectroscopy data.

Journal of neuroscience methods·2026
Same author

Long-term outcomes of cochlear implantation in children with cochlear nerve deficiency: auditory speech performance and predictive factors over 5 years.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2026
Same author

Longitudinal Speech Outcomes in Cochlear Implant Recipients Are Associated With Neural Factors Identified Using Psychophysics and Functional Brain Imaging.

Ear and hearing·2026
Same author

Dynamic functional connectivity following cochlear implantation: Predicting speech outcomes and exploring brain network dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2025

Related Experiment Video

Updated: Jul 8, 2025

Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design
06:18

Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

Published on: December 3, 2020

3.7K

A Parametric Model for Characterizing Time-Variant Single Trials of Block-Design fNIRS Experiments.

Tommy Peng, Jamal Esmaelpoor, Darren Mao

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study introduces a new parametric Gaussian model to analyze time-variant brain activity in functional near-infrared spectroscopy (fNIRS) block-design experiments. The model accurately captures trial-to-trial fNIRS response changes, overcoming limitations of traditional time-invariant analysis methods.

    More Related Videos

    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
    08:19

    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

    Published on: October 20, 2023

    1.1K
    Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
    11:31

    Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

    Published on: December 5, 2014

    15.2K

    Related Experiment Videos

    Last Updated: Jul 8, 2025

    Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design
    06:18

    Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

    Published on: December 3, 2020

    3.7K
    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
    08:19

    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

    Published on: October 20, 2023

    1.1K
    Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
    11:31

    Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

    Published on: December 5, 2014

    15.2K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Functional near-infrared spectroscopy (fNIRS) commonly uses block-design paradigms.
    • Traditional analysis methods like GLM and WA assume a time-invariant brain system, which is often inaccurate.
    • This assumption limits the understanding of dynamic brain responses during experiments.

    Purpose of the Study:

    • To develop and validate a parametric Gaussian model for quantifying time-variant brain activity in block-design fNIRS.
    • To address the limitations of traditional time-invariant analysis techniques in fNIRS.
    • To enable the study of dynamic, trial-to-trial changes in brain responses.

    Main Methods:

    • Proposed a parametric Gaussian model to quantify time-variant behavior in fNIRS data.
    • Validated the model using simulated data across various signal-to-noise ratios (SNRs).
    • Applied the model to recorded data from an auditory block-design fNIRS experiment.

    Main Results:

    • The proposed model successfully characterized Gaussian-like fNIRS signal features at SNRs ≥3dB.
    • Analysis of recorded data revealed statistically significant, quantitative changes in fNIRS responses across trials.
    • Model parameter values aligned with visual inspection of individual trial data, confirming its effectiveness.

    Conclusions:

    • The parametric Gaussian model effectively captures trial-to-trial differences in fNIRS responses.
    • This technique allows for the investigation of time-variant brain activity within block-design fNIRS studies.
    • Researchers can now better study dynamic neural processes using established block-design fNIRS paradigms.