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 Experiment Video

Updated: May 14, 2026

fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

Using NIRS as a predictor for EEG-based BCI performance.

Siamac Fazli1, Jan Mehnert, Jens Steinbrink

  • 1Department of Computer Science, Technical University Berlin, Germany. fazli@cs.tu-berlin.de

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

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

CheMLT-F: multitask learning in biochemistry through transformer fusion.

Journal of cheminformatics·2026
Same author

Influence of anesthetic agent and burst suppression on postoperative delirium in elderly patients: a prospective cohort study with automated EEG analysis.

Frontiers in aging neuroscience·2026
Same author

Data-driven head model individualization from digitized electrode positions or photogrammetry improves M/EEG source localization accuracy.

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

Migraine is a disorder of function: No evidence for structural alterations within the central nervous system in migraine.

Headache·2025
Same author

Determining the structure of functionalized graphene for tailored thermomechanical properties using ML techniques.

RSC advances·2025
Same author

Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.

Imaging neuroscience (Cambridge, Mass.)·2025
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

Near-Infrared Spectroscopy (NIRS) activity predicts electroencephalography (EEG) brain-computer interface (BCI) control fluctuations. This prediction improves EEG-based BCI classifier robustness and stability.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-Computer Interfaces (BCIs) enable control via neural signals.
  • EEG and NIRS are common neuroimaging modalities for BCIs.
  • BCI performance often suffers from fluctuations and instability.

Purpose of the Study:

  • To investigate the predictive power of NIRS on EEG-based BCI performance.
  • To develop improved BCI classifiers using NIRS-based predictions.
  • To enhance the stability and robustness of sensory-motor BCI control.

Main Methods:

  • Multimodal recordings of EEG and NIRS from 14 subjects.
  • Analysis of sensory-motor based BCI paradigms.
  • Development of novel EEG classifiers informed by NIRS data.

More Related Videos

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task
13:18

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Published on: May 24, 2020

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

Related Experiment Videos

Last Updated: May 14, 2026

fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task
13:18

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Published on: May 24, 2020

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

  • Quantitative evaluation of classification accuracy and stability.

    • Main Results:

    • NIRS activity significantly predicts fluctuations in EEG-based BCI control.
    • NIRS-informed classifiers demonstrate enhanced classification accuracy.
    • The proposed method reduces performance variability, increasing overall BCI stability.

    Conclusions:

    • NIRS provides valuable predictive information for EEG-based BCIs.
    • Integrating NIRS and EEG can lead to more reliable BCI systems.
    • This approach offers a pathway to more stable and robust brain-computer interfaces.