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

Force Classification01:22

Force Classification

2.0K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.0K
Motor Units01:13

Motor Units

6.7K
The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
6.7K
Motor Units00:46

Motor Units

61.1K
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
61.1K
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.2K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.2K
Classification of Systems-I01:26

Classification of Systems-I

450
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
450
Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Applications of electromyography in Amyotrophic Lateral Sclerosis: A systematic review.

PloS one·2026
Same author

Quantifying Inter- and Intra-Subject Variability of Sensorimotor Desynchronization Induced by Median Nerve Stimulation and Motor Imagery for BCI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Reliable predictor of BCI motor imagery performance using median nerve stimulation.

Journal of neural engineering·2025
Same author

Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.

Brain computer interfaces (Abingdon, England)·2024
Same author

Electromyography as a tool to motion analysis for people with Amyotrophic Lateral Sclerosis: A protocol for a systematic review.

PloS one·2024
Same author

Impact of the baseline temporal selection on the ERD/ERS analysis for Motor Imagery-based BCI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same journal

Evaluation of an open-face 8-channel transmit 64-channel receive 7T head coil for neuroimaging.

Frontiers in neuroscience·2026
Same journal

Acoustic stimulation in pain management: neurobiological mechanisms and clinical applications-a narrative review.

Frontiers in neuroscience·2026
Same journal

Local brain connectome parameters across the spectrum of clinical cognitive decline.

Frontiers in neuroscience·2026
Same journal

Body mass index affects EEG microstate dynamics through blood viscosity in high-altitude environments.

Frontiers in neuroscience·2026
Same journal

Disrupted glymphatic function and its relationship with sleep and cognitive impairment in ME/CFS assessed via DTI-ALPS.

Frontiers in neuroscience·2026
Same journal

Neuromorphic-inspired multi-view global-local fusion for IR-UWB radar dynamic gesture recognition.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Nov 25, 2025

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

1.5K

Multiclass Classification Based on Combined Motor Imageries.

Cecilia Lindig-León1,2, Sébastien Rimbert1, Laurent Bougrain1

  • 1Université de Lorraine, CNRS, LORIA, Inria, Nancy, France.

Frontiers in Neuroscience
|December 17, 2020
PubMed
Summary
This summary is machine-generated.

New multilabel approaches for motor imagery (MI) enhance brain-computer interfaces (BCIs). These methods improve accuracy and increase the number of commands for better user interaction with BCIs.

Keywords:
brain-computer interface (BCI)combined motor imageriescommon spatial pattern (CSP)electroencephalography (EEG)multilabel classification

More Related Videos

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

1.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K

Related Experiment Videos

Last Updated: Nov 25, 2025

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

1.5K
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

1.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K

Area of Science:

  • Neuroscience and Biomedical Engineering
  • Brain-Computer Interfaces (BCIs)
  • Signal Processing

Background:

  • Motor imagery (MI) enables self-paced brain-computer interfaces (BCIs) for intuitive interaction.
  • Non-invasive MI-BCIs struggle with more than three commands due to limited distinct motor activities and noisy electroencephalography (EEG) signals.
  • Existing methods face challenges in differentiating closely related motor activities and improving classification accuracy.

Purpose of the Study:

  • To propose novel multilabel approaches for motor imagery to overcome limitations in non-invasive BCI command expansion.
  • To enhance the signal-to-noise ratio (SNR) in EEG signals for improved classification performance.
  • To increase the number of identifiable motor activities for more intuitive and continuous BCI interaction.

Main Methods:

  • Introduction of combined motor imagery (MI) involving multiple body parts simultaneously.
  • Development of two novel multilabel Common Spatial Pattern (CSP) algorithms: MC2CMI and MC2SMI.
  • Recording EEG signals from seven subjects during an 8-class experiment, including rest and combinations of left hand, right hand, and feet MI.
  • Transforming the 8-class problem into three binary classification problems for CSP application.
  • Merging features from binary classifiers using an 8-class linear discriminant analysis (LDA).

Main Results:

  • Both MC2CMI and MC2SMI approaches achieved higher accuracy compared to classic pair-wise (PW) and one-vs.-all (OVA) methods across all subjects.
  • The MC2SMI method demonstrated significantly accelerated calibration time by excluding combined MIs during training.
  • The proposed multilabel strategies effectively increased the number of distinguishable motor imagery tasks.

Conclusions:

  • Multilabel approaches are a promising solution for expanding the command capabilities of non-invasive MI-based BCIs.
  • The MC2CMI and MC2SMI methods offer improved accuracy and efficiency for BCI applications.
  • Proper modulation of brain activity combined with advanced signal processing techniques can lead to better BCI interaction.