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 30, 2026

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

Learning from feedback training data at a self-paced brain-computer interface.

Haihong Zhang1, Sidath Ravindra Liyanage, Chuanchu Wang

  • 1Institute for Infocomm Research, Agency for Science, Technology and Research, 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632. hhzhang@i2r.a-star.edu.sg

Journal of Neural Engineering
|July 21, 2011
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

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 author

Decoding Covert Speech From EEG by Functional Areas Spatio-Temporal Transformer.

IEEE journal of biomedical and health informatics·2026
Same author

Bioinspired Heat-Induced Viscoelasticity-Switchable Electrodes for Conformal Brain-Computer Interfaces.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-Based Gait Decoding.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same journal

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026
Same journal

Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.

Journal of neural engineering·2026
Same journal

Developing a binary communication protocol between biological neural networks using virtual white matter.

Journal of neural engineering·2026
Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026
Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026
Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026
See all related articles

Adapting feature extraction is crucial for brain-computer interfaces (BCI). This study introduces a new method to create better feature spaces from feedback data, significantly reducing prediction errors in motor imagery tasks.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCI) face challenges due to inherent changes in brain signals between calibration and feedback sessions.
  • Previous research primarily focused on classifier adaptation, neglecting feature extraction adaptation in self-paced BCI.

Purpose of the Study:

  • To investigate the feasibility and importance of adapting feature extraction in self-paced motor imagery BCI.
  • To develop a supervised method for constructing an adaptive feature space using feedback data.

Main Methods:

  • Utilized a novel self-paced motor imagery BCI with an idle state for calibration and feedback training.
  • Proposed a supervised method maximizing mutual information between feedback, target signals, and EEG to learn spatial-spectral filtering parameters.

More Related Videos

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Related Experiment Videos

Last Updated: May 30, 2026

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

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

  • Derived a gradient-based optimization algorithm for feature space adaptation.

    • Main Results:

    • Online results indicated that calibration-based feature spaces become ineffective during feedback sessions.
    • The proposed method successfully constructed effective feature spaces capturing discriminative information from feedback data.
    • Significant reduction in prediction error was achieved using the newly extracted features.

    Conclusions:

    • Adapting feature extraction is vital for robust BCI performance, complementing classifier adaptation.
    • The proposed mutual information-based method offers a promising approach for real-time feature space adaptation in BCI systems.
    • This work highlights the potential for improved BCI accuracy through adaptive feature engineering.