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

Error perception classification in Brain-Computer Interfaces using CNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021

Same author

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

Computers in biology and medicine·2021

Same author

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Reviews in cardiovascular medicine·2021

Same author

Combining Deep Learning with Handcrafted Features for Cell Nuclei Segmentation<sup>.</sup>

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020

Same author

Interphase Cell Cycle Staging using Deep Learning<sup>.</sup>

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020

Same author

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.

Computers in biology and medicine·2020

Search research articles

Related Experiment Video

Updated: May 14, 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

Phase-locking factor in a motor imagery Brain-Computer Interface.

Carlos Carreiras¹, Luis Borges de Almeida, J Miguel Sanches

¹Institute for Systems and Robotics, IST, Lisbon, Portugal.

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

Summary

This summary is machine-generated.

This study introduces a new Brain-Computer Interface (BCI) method using Phase-Locking Factor (PLF) from electroencephalogram (EEG) data. PLF achieved higher accuracy (86%) than traditional band power (70%) for detecting motor imagery, offering a promising communication channel for locked-in patients.

More Related Videos

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

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

Related Experiment Videos

Last Updated: May 14, 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

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

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

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Brain-Computer Interfaces (BCIs) offer communication for patients with severe motor impairments.
Motor Imagery (MI) is a key BCI control strategy, modulating electroencephalogram (EEG) signals.
Event-Related Desynchronization (ERD) is a common EEG feature for MI detection.

Purpose of the Study:

To introduce and evaluate a novel BCI feature extraction method using Phase-Locking Factor (PLF) from EEG data.
To compare the efficacy of PLF features against traditional band power features for MI detection.
To assess the potential of PLF for improving BCI performance in locked-in patients.

Main Methods:

EEG data were collected from 6 subjects performing 7 distinct motor tasks.
Two feature extraction methods were applied: traditional band power and the proposed Phase-Locking Factor (PLF).
Support Vector Machine (SVM) classifiers, arranged hierarchically, were used for feature classification.

Main Results:

The PLF feature extraction method achieved a higher average classification accuracy of approximately 86%.
Traditional band power features yielded an average accuracy of approximately 70%.
PLF demonstrated superior performance in discriminating between different motor imagery tasks.

Conclusions:

Phase-Locking Factor (PLF) shows significant promise as an effective feature for Brain-Computer Interface (BCI) systems.
The proposed PLF-based method offers a potential improvement over traditional band power analysis for motor imagery detection.
Further research is warranted to fully explore the capabilities of PLF in advanced BCI applications.