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

Wearable Functional Near-Infrared Spectroscopy (fNIRS) Monitoring of Prefrontal Activation and Connectivity During Purpose-Driven Consumption.

Sensors (Basel, Switzerland)·2026
Same author

ERK3/MAPK6 promotes triple-negative breast cancer progression through collective migration and EMT plasticity.

Frontiers in oncology·2025
Same author

Investigating the neurophysiological effects of active noise cancellation on concentration in noisy environments using functional near-infrared spectroscopy.

Hearing research·2025
Same author

Analysis of consumer purchase intentions using functional near-infrared spectroscopy(fNIRS): A neuromarketing study on the aesthetic packaging of Korean red ginseng products.

PloS one·2025
Same author

Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study.

Biomedical engineering letters·2023
Same author

Classifying Children with ADHD Based on Prefrontal Functional Near-infrared Spectroscopy Using Machine Learning.

Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology·2023

Related Experiment Video

Updated: Dec 12, 2025

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

15.5K

Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces.

Jaeyoung Shin1

  • 1Department of Electronic Engineering, Wonkwang University, Iksan, South Korea.

Frontiers in Human Neuroscience
|August 9, 2020
PubMed
Summary
This summary is machine-generated.

Random subspace ensemble learning enhances functional near-infrared spectroscopy-based brain-computer interfaces (fNIRS-BCIs). This method improves brain-computer interface performance by optimizing feature selection for better signal processing.

Keywords:
brain-computer interfaceensemble learningfunctional near-infrared spectroscopylinear discriminant analysisrandom subspacesupport vector machine

More Related Videos

Author Spotlight: Advancing Upper Limb Rehabilitation in Patients with Right Hemisphere Damage Using Assisted Active Exercise
04:43

Author Spotlight: Advancing Upper Limb Rehabilitation in Patients with Right Hemisphere Damage Using Assisted Active Exercise

Published on: February 9, 2024

1.4K
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

1.1K

Related Experiment Videos

Last Updated: Dec 12, 2025

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

15.5K
Author Spotlight: Advancing Upper Limb Rehabilitation in Patients with Right Hemisphere Damage Using Assisted Active Exercise
04:43

Author Spotlight: Advancing Upper Limb Rehabilitation in Patients with Right Hemisphere Damage Using Assisted Active Exercise

Published on: February 9, 2024

1.4K
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

1.1K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Functional near-infrared spectroscopy-based brain-computer interfaces (fNIRS-BCIs) offer a non-invasive method for brain-computer interaction.
  • Improving the performance and accuracy of fNIRS-BCIs is crucial for their clinical and practical applications.

Purpose of the Study:

  • To investigate the feasibility of the random subspace ensemble learning method for enhancing fNIRS-BCI performance.
  • To explore the effectiveness of temporal features (mean, slope, variance) in fNIRS signal processing for BCIs.

Main Methods:

  • Feature vectors were constructed using temporal characteristics of fNIRS chromophores, specifically mean and slope.
  • A linear support vector machine was used as a strong learner, while linear discriminant analysis served as weak learners.
  • The random subspace method was applied to select random feature subsets for training weak learners.

Main Results:

  • The random subspace ensemble learning method demonstrated feasibility in improving fNIRS-BCI performance.
  • Utilizing temporal features like mean and slope contributed to the system's implementation.
  • Ensemble learning with random subspace feature selection proved beneficial for overall fNIRS-BCI enhancement.

Conclusions:

  • Random subspace ensemble learning is a viable strategy to boost the performance of fNIRS-BCIs.
  • The study highlights the importance of appropriate feature selection and ensemble methods in BCI development.
  • This approach offers a promising direction for advancing fNIRS-BCI technology.