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

You might also read

Related Articles

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

Sort by
Same author

Giant Ulcerated Polypoidal Cellular Fibrous Histiocytoma and Clustered Multiple Cellular Fibrous Histiocytomas in Segmental Distribution: An Uncommon Presentation with Benign Biological Behaviour.

Indian journal of dermatology·2025
Same author

Levothyroxine-Induced Systemic Lupus Erythematosus in a Patient with Hypothyroidism: A Rare Case Report.

Indian dermatology online journal·2024
Same author

Non-invasive glucose prediction and classification using NIR technology with machine learning.

Heliyon·2024
Same author

Idiopathic Cutaneous Pseudolymphoma.

Indian journal of dermatology·2023
Same author

A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images.

Multimedia tools and applications·2023
Same author

Morphea Profunda Masquerading as Prurigo Nodularis: An Uncommon Presentation.

Indian journal of dermatology·2022

Related Experiment Video

Updated: Jul 6, 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

1.0K

EEG-BCI-based motor imagery classification using double attention convolutional network.

V Sireesha1, V V Satyanarayana Tallapragada2, M Naresh3

  • 1Department of Computer Science and Engineering, School of Technology, GITAM University, Hyderabad, India.

Computer Methods in Biomechanics and Biomedical Engineering
|January 2, 2024
PubMed
Summary

This study enhances brain-computer interfaces (BCI) for motor imagery (MI) by refining signal processing. The novel approach achieves high accuracy in classifying EEG signals, improving BCI performance.

Keywords:
EEG signalMotor imageryarithmetic operation optimizationcommon spatial patternconvolutional netdouble attentionmodified least mean squarepearson correlation coefficient

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

1.3K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K

Related Experiment Videos

Last Updated: Jul 6, 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

1.0K
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

1.3K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K

Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Brain-computer interfaces (BCI) are crucial for understanding neurological phenomena during motor imagery (MI).
  • Existing signal processing methods struggle with noise and feature extraction in electroencephalography (EEG) data.
  • Accurate classification of EEG signals is essential for effective BCI applications.

Purpose of the Study:

  • To improve and diversify signal processing techniques for BCI systems utilizing MI.
  • To develop a robust method for noise reduction and feature extraction from EEG data.
  • To enhance the classification accuracy and efficiency of EEG-based BCI models.

Main Methods:

  • Pre-processing using Modify Least Mean Square (M-LMS) to remove noise like intermodulation and crosstalk.
  • Feature extraction using Common Spatial Pattern (CSP) and Pearson's Correlation Coefficient (PCC).
  • Feature selection and dimensionality reduction via Extended Arithmetic operation optimization (ExAo).
  • Classification using Double Attention Convolutional Neural Networks (DAttnConvNet) with an attention mechanism.

Main Results:

  • The proposed model achieved high classification accuracies on EEG motor imagery datasets: 99.98% for Baseline (B), 99.82% for Imagined movement of a right fist (R), and 99.61% for Imagined movement of both fists (RL).
  • The model demonstrated superior performance compared to other models on EEG datasets, achieving 97.94% accuracy.
  • The attention mechanism in DAttnConvNet effectively selected and optimized features, enhancing classification.

Conclusions:

  • The developed signal processing framework significantly improves BCI performance for motor imagery tasks.
  • The combination of M-LMS, CSP, PCC, ExAo, and DAttnConvNet offers a robust and accurate approach for EEG signal classification.
  • This research contributes to advancing BCI technology through enhanced signal processing and machine learning techniques.