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

Gene expression and metadata based identification of key genes for lung cancer, COPD, and IPF using machine learning and statistical models.

PloS one·2026
Same author

Deep Learning-Based Eye-Writing Recognition with Improved Preprocessing and Data Augmentation Techniques.

Sensors (Basel, Switzerland)·2025
Same author

Bangla Speech Emotion Recognition Using Deep Learning-Based Ensemble Learning and Feature Fusion.

Journal of imaging·2025
Same author

A Comprehensive Methodological Survey of Human Activity Recognition Across Diverse Data Modalities.

Sensors (Basel, Switzerland)·2025
Same author

Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.

PloS one·2025
Same author

Stacked CNN-based multichannel attention networks for Alzheimer disease detection.

Scientific reports·2025

Related Experiment Video

Updated: Jun 17, 2025

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

1.2K

Exploring Feature Selection and Classification Techniques to Improve the Performance of an

Md Humaun Kabir1, Nadim Ibne Akhtar1, Nishat Tasnim1

  • 1Department of Computer Science and Engineering, Bangamata Sheikh Fojilatunnesa Mujib Science & Technology University, Jamalpur 2012, Bangladesh.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
Summary

This study introduces an advanced method for classifying motor imagery (MI) using electroencephalogram (EEG) signals in brain-computer interfaces (BCIs). The novel approach enhances accuracy by effectively extracting and selecting relevant brain signal features.

Keywords:
Brain-computer Interface (BCI)Electroencephalography (EEG)Feature SelectionLinear Discriminant Analysis (LDA)Machine Learning (ML)Motor Imagery (MI)Relief-F

More Related Videos

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.0K
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

921

Related Experiment Videos

Last Updated: Jun 17, 2025

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

1.2K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.0K
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

921

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Classifying motor imagery (MI) activities using electroencephalogram (EEG) signals is crucial for brain-computer interfaces (BCIs).
  • Existing BCI systems struggle with accuracy due to non-discriminative and ineffective features extracted from EEG data.
  • Developing robust feature extraction and selection methods is essential for improving BCI performance.

Purpose of the Study:

  • To propose a novel multiband decomposition-based feature extraction and selection method for enhanced MI classification in BCIs.
  • To address the challenge of high dimensionality and ineffectiveness of features in current EEG-based BCI systems.
  • To improve the accuracy and reliability of motor imagery recognition for BCI applications.

Main Methods:

  • Preprocessing EEG signals and decomposing them into four sub-bands.
  • Applying Common Spatial Pattern (CSP) technique for narrowband feature extraction within each sub-band.
  • Utilizing the Relief-F algorithm for effective feature selection to reduce dimensionality, followed by advanced classification.

Main Results:

  • The proposed method demonstrated superior performance in classifying MI tasks across three benchmark EEG datasets.
  • Achieved higher classification accuracy compared to existing state-of-the-art BCI systems.
  • Successfully reduced feature dimensionality while enhancing the discriminative power of selected features.

Conclusions:

  • The developed multiband decomposition and feature selection approach significantly improves MI classification accuracy in BCIs.
  • The method offers an effective solution for handling complex EEG data and overcoming limitations of traditional feature extraction techniques.
  • This work contributes to the advancement of more reliable and efficient brain-computer interfaces for individuals with motor impairments.