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

Classification of Systems-I01:26

Classification of Systems-I

184
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:
184
Classification of Systems-II01:31

Classification of Systems-II

144
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
144
Force Classification01:22

Force Classification

1.2K
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,...
1.2K
Aggregates Classification01:29

Aggregates Classification

317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317
Structural Classification of Joints01:20

Structural Classification of Joints

3.4K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.4K
Classification of Signals01:30

Classification of Signals

456
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...
456

You might also read

Related Articles

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

Sort by
Same author

Efficacy of Intermittent Theta-Burst Stimulation for Prolonged Disorders of Consciousness: A Prospective, Randomized, Controlled Trial.

Annals of clinical and translational neurology·2026
Same author

Designing an oxidase toolbox for site-directed oxidation of taxanes.

Nature communications·2025
Same author

Corticomuscular Coupling Analysis of Dynamic Balance During Eccentric and Concentric Muscle Contractions.

The European journal of neuroscience·2025
Same author

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.

Computer methods in biomechanics and biomedical engineering·2024
Same author

Determinants of Maximum Magnetic Anomaly Detection Distance.

Sensors (Basel, Switzerland)·2024
Same author

Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient.

Journal of neural engineering·2023
Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026
Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026
Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026
Same journal

A robot-assisted eye positioning method with high precision and repeatability for ocular particle therapy: mechanical and geometric assessment.

Medical & biological engineering & computing·2026
Same journal

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026
Same journal

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Medical & biological engineering & computing·2026
See all related articles

Related Experiment Video

Updated: Jun 30, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Optimizing motion imagery classification with limited channels using the common spatial pattern-based integrated

Shishi Chen1,2, Xugang Xi1,2, Ting Wang3,4

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.

Medical & Biological Engineering & Computing
|March 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method combining Variational Mode Decomposition (VMD) and Phase Space Reconstruction (PSR) to improve electroencephalogram (EEG) signal analysis for motor imagery. The enhanced approach boosts classification accuracy in brain-computer interfaces.

Keywords:
Brain-computer interfaces (BCI)Common spatial patternMotor imageryPhase space reconstructionVariational mode decomposition

More Related Videos

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

695
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

596

Related Experiment Videos

Last Updated: Jun 30, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K
Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

695
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

596

Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Motor imagery electroencephalogram (EEG) signal classification is crucial for brain-computer interfaces (BCIs).
  • Traditional Common Spatial Pattern (CSP) algorithms face limitations due to frequency band selection and limited channel data.
  • Non-Gaussian and nonlinear characteristics of EEG signals pose challenges for standard CSP feature extraction.

Purpose of the Study:

  • To propose a novel method integrating Variational Mode Decomposition (VMD), Phase Space Reconstruction (PSR), and CSP to overcome limitations in EEG signal analysis.
  • To enhance feature extraction from limited EEG channels by leveraging signal decomposition and data augmentation.
  • To improve the accuracy of motor imagery classification in BCIs.

Main Methods:

  • Raw EEG signals were decomposed into multiple Intrinsic Mode Functions (IMFs) using VMD for signal enhancement.
  • Phase Space Reconstruction (PSR) was applied to augment the effective number of data channels.
  • The enhanced signals were processed using CSP for spatial feature extraction, followed by Convolutional Neural Networks (CNNs) for action decoding.

Main Results:

  • The proposed VMD-PSR-CSP method achieved an average classification accuracy of 82.30% on self-collected EEG data.
  • Validation on the BCI Competition IV dataset 2b yielded an average classification accuracy of 87.49%.
  • Results demonstrate the effectiveness of the integrated approach in handling nonlinear and non-Gaussian EEG data with limited channels.

Conclusions:

  • The novel VMD-PSR-CSP integration effectively addresses the limitations of traditional CSP in motor imagery BCI.
  • The method shows significant potential for improving the performance of BCIs by enhancing EEG feature extraction.
  • The findings confirm the feasibility and improved effectiveness of the proposed signal processing and classification strategy.