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

Hybrid Neural Network Architecture for Automated Liver and Tumor Segmentation Using Ensemble Learning on CT Images.

Biomimetics (Basel, Switzerland)·2026
Same authorSame journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026
Same author

Diagnostic Accuracy of Ultrasonography for Identification of Skull Fracture in Children: A Systematic Review and Meta-analysis.

Archives of academic emergency medicine·2026
Same author

Deep Learning-Based Automatic Segmentation of Ischemic Stroke Lesions in CT Perfusion Imaging.

Biomimetics (Basel, Switzerland)·2026
Same author

Developing an Artificial Intelligence Solution to Autosegment the Edentulous Maxillary Bone for Implant Planning.

European journal of dentistry·2026
Same author

GAN-Based Cross-Modality Brain MRI Synthesis: Paired Versus Unpaired Training and Comparison with Diffusion and Transformer Models.

Biomimetics (Basel, Switzerland)·2026
Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026
Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026
Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. Improved Automatic Deep Model For Automatic Detection Of Movement Intention From Eeg Signals.
  1. Home
  3. Improved Automatic Deep Model For Automatic Detection Of Movement Intention From Eeg Signals.

Related Experiment Video

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.0K

Improved Automatic Deep Model for Automatic Detection of Movement Intention from EEG Signals.

Lida Zare Lahijan1, Saeed Meshgini1, Reza Afrouzian2

  • 1Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran.

Biomimetics (Basel, Switzerland)
|August 27, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel brain-computer interface (BCI) method using deep learning for automatic movement intention detection from electroencephalogram (EEG) signals during finger tapping, achieving high accuracy.

Keywords:
BCICNNEEGfinger tappinggraph theorymovement intention

More Related Videos

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.5K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

Related Experiment Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.0K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.5K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Automated movement intention identification is vital for brain-computer interface (BCI) applications.
  • Assisting patients with mobility impairments to regain movement is a key goal.
  • Current methods require robust feature extraction from neural signals.

Purpose of the Study:

  • To develop a novel approach for automatic movement intention identification using electroencephalogram (EEG) signals.
  • To create a deep learning model capable of accurately decoding motor intentions from finger tapping.
  • To enhance the applicability of BCIs in real-world scenarios, including noisy environments.

Main Methods:

  • A database of EEG signals was created from left finger taps, right finger taps, and a resting state.
  • A novel model integrating graph theory and deep convolutional networks was designed.
  • The architecture comprises six deep convolutional graph layers for feature extraction.

    • Main Results:

    • The model achieved 98% accuracy in binary classification (left vs. right finger tapping).
    • It attained 92% accuracy in a three-class classification task (left tap, right tap, rest).
    • The model demonstrated significant resilience in noisy conditions compared to recent studies.

    Conclusions:

    • The proposed deep convolutional graph network effectively decodes motor-related brain activity from EEG.
    • This approach shows promise for reliable online BCI applications, even with signal noise.
    • The findings contribute to advancing BCI technology for motor rehabilitation and assistance.