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

Application of Filter Bank to Improve Fatigue Monitoring in Wearable EEG-Based Brain-Computer Interface.

NeuroSci·2026
Same author

Feasibility, acceptability, and functional outcomes of a home-based exergaming telerehabilitation program for adults with functional disabilities: a multi-center single-arm pilot study.

Journal of neuroengineering and rehabilitation·2026
Same author

Telerehabilitation robotics for upper limb rehabilitation after stroke (TRUST): a multi-site pragmatic trial protocol.

Frontiers in neurology·2026
Same author

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Decoupled Hierarchical Distillation for Multimodal Emotion Recognition.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

EEG-to-gait decoding via phase-aware representation learning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Supporting human-agent communication for explainable planning in spatial-temporal planning problems.

Neural computing & applications·2026
Same journal

Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition.

Neural computing & applications·2026
Same journal

Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.

Neural computing & applications·2026
Same journal

Balancing misclassification errors in image-based inference using problem domain semantics and a nested cascade architecture.

Neural computing & applications·2025
Same journal

Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization.

Neural computing & applications·2025
Same journal

A fairness scale for real-time recidivism forecasts using a national database of convicted offenders.

Neural computing & applications·2025
See all related articles

Related Experiment Video

Updated: Feb 20, 2026

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

2.2K

Facilitating motor imagery-based brain-computer interface for stroke patients using passive movement.

Mahnaz Arvaneh1, Cuntai Guan2, Kai Keng Ang2

  • 1Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.

Neural Computing & Applications
|October 21, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm, filter bank data space adaptation (FB-DSA), to improve motor imagery-based brain-computer interface (MI-BCI) calibration for stroke rehabilitation. Calibrating BCIs using passive movement (PM) with FB-DSA enhances classification accuracy, reducing patient fatigue and increasing therapeutic time.

Keywords:
AdaptationBrain–computer interfaceMotor imageryPassive movementStroke rehabilitation

More Related Videos

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.9K
Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

1.8K

Related Experiment Videos

Last Updated: Feb 20, 2026

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

2.2K
Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.9K
Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

1.8K

Area of Science:

  • Neuroscience
  • Rehabilitation Engineering
  • Biomedical Signal Processing

Background:

  • Motor imagery-based brain-computer interfaces (MI-BCIs) aid stroke recovery but require lengthy calibration.
  • Passive movement (PM) elicits similar EEG patterns to motor imagery (MI) with less patient effort.
  • Reducing calibration time and fatigue is crucial for effective stroke rehabilitation.

Purpose of the Study:

  • To investigate the effectiveness of calibrating MI-BCIs using PM data for stroke patients.
  • To introduce and evaluate a novel adaptive algorithm, filter bank data space adaptation (FB-DSA), for calibrating MI-BCIs.
  • To compare the classification accuracy of PM-calibrated and MI-calibrated models.

Main Methods:

  • Development of the filter bank data space adaptation (FB-DSA) algorithm to minimize distribution differences between MI and PM EEG data.
  • Linear transformation of band-pass-filtered EEG data using FB-DSA.
  • Offline evaluation of the FB-DSA algorithm using data from 16 healthy subjects and 6 stroke patients.

Main Results:

  • The FB-DSA algorithm significantly improved classification accuracies for both PM and MI calibrated models (p < 0.05).
  • PM-calibrated models adapted with FB-DSA outperformed MI-calibrated models by 2.3% in healthy subjects and 4.5% in stroke patients.
  • A potentially stronger disparity between MI and PM in stroke patients suggests a greater need for FB-DSA in stroke rehabilitation.

Conclusions:

  • FB-DSA is an effective method for adapting PM data to calibrate MI-BCIs, reducing calibration burden for stroke patients.
  • This approach enhances BCI performance and increases the time available for therapeutic interaction in stroke rehabilitation.
  • The findings support the use of FB-DSA for developing more efficient and effective BCI-based rehabilitation strategies for stroke survivors.