Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Rationale and current status of fecal microbiota transplantations for Parkinson's disease.

Journal of Parkinson's disease·2026

Same author

A Conversational Brain-Artificial Intelligence Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same author

Auto-activation deficit with pallidal degeneration due to manganese toxicity: a case report.

Acta neurologica Belgica·2026

Same author

Genetic Landscape of Monogenic Parkinson's Disease in the African Population-A Systematic Review.

Movement disorders : official journal of the Movement Disorder Society·2026

Same author

Faecal microbiota transplant for Parkinson's disease.

Brain : a journal of neurology·2026

Same author

Vestibular functioning in people with Parkinson's disease.

International journal of audiology·2026

Search research articles

Related Experiment Video

Updated: May 28, 2026

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

Published on: September 1, 2023

Multisubject learning for common spatial patterns in motor-imagery BCI.

Dieter Devlaminck¹, Bart Wyns, Moritz Grosse-Wentrup

¹Electrical Energy, Systems and Automation, Ghent University, Gent, Belgium. ddvlamin@gmail.com

Computational Intelligence and Neuroscience

|October 19, 2011

Summary

Multisubject machine learning improves motor-imagery brain-computer interfaces (BCIs) by reducing the need for extensive subject-specific training data. This approach enhances spatial filter learning, especially with limited calibration trials.

More Related Videos

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

Published on: May 10, 2024

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Related Experiment Videos

Last Updated: May 28, 2026

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

Published on: September 1, 2023

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

Published on: May 10, 2024

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Motor-imagery brain-computer interfaces (BCIs) are crucial for assistive technologies.
Common Spatial Pattern (CSP) filtering is a standard preprocessing step in BCIs.
CSP requires time-consuming subject-specific calibration data.

Purpose of the Study:

To introduce and evaluate a multisubject machine learning approach for CSP.
To reduce the amount of calibration data needed for new BCI users.
To enhance the efficiency of BCI system setup.

Main Methods:

Developed a multitask (multisubject) learning algorithm for CSP.
Applied the algorithm to two BCI datasets.
Compared performance against traditional subject-specific CSP.

Main Results:

Multisubject CSP demonstrated clear improvements in certain subjects.
Performance gains were particularly noticeable with a low number of training trials.
The method effectively leverages data from multiple subjects.

Conclusions:

Multisubject learning offers a promising solution to reduce BCI calibration time.
This technique can enhance BCI usability and accessibility.
Further research can optimize multisubject CSP for diverse BCI applications.