Search research articles

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

Dynamic Hydrogen-Bonding Switching Enables Crystal Transformation for Multi-Stimuli Responsive Fluorescence.

Angewandte Chemie (International ed. in English)·2026

Same author

Time-domain waveform recovery for integrated optical waveguide pulse electric field sensors.

Applied optics·2026

Same author

Leveraging LLMs for Efficient Data Structure Standardization in Chinese Medical Examination Reports.

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

Cell-Selective Targeting Chimeras (SelecTAC<sup>B</sup>) for Membrane Protein Degradation on B Cells.

Journal of the American Chemical Society·2025

Same author

Causal effects of cholecystectomy on the lipidome: cross-sectional and mendelian randomization studies.

Surgical endoscopy·2025

Same author

Riboflavin-mediated reactivation of aged nano zero-valent iron by Shewanella oneidensis MR-1 for sustained Cr(VI) detoxification.

Bioresource technology·2025

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding.

Xingliang Tang^1,2, Xianrui Zhang³

¹School of Information Science and Engineering, LanZhou University, Lanzhou 730000, China.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

This summary is machine-generated.

This study introduces a novel deep learning framework for decoding motor imagery (MI) electroencephalogram (EEG) signals. The method effectively adapts models across subjects, improving brain-computer interface (BCI) performance.

Keywords:

convolutional neural network domain adaptation electroencephalogram (EEG)motor imagery (MI)signal classification

More Related Videos

A Method to Study Adaptation to Left-Right Reversed Audition

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Related Experiment Videos

Last Updated: Nov 27, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

A Method to Study Adaptation to Left-Right Reversed Audition

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Decoding electroencephalogram (EEG) signals for motor imagery (MI) in brain-computer interfaces (BCIs) is complex due to signal non-stationarity.
Deep learning excels at automatic feature extraction from EEG, but struggles with limited labeled data and cross-subject generalization.

Purpose of the Study:

To develop a novel deep learning framework for robust motor imagery EEG signal decoding.
To address the challenge of limited labeled data and subject-specific variations in EEG decoding.

Main Methods:

A conditional domain adaptation neural network (CDAN) framework was proposed.
A densely connected convolutional neural network (ConvNet) extracted high-level features.
A conditional domain discriminator was employed adversarially with a label classifier to learn shared intra-subject features.

Main Results:

The CDAN model demonstrated efficient classification of target subject EEG signals after training on data from other subjects.
Competitive performance was achieved on the High Gamma Dataset compared to state-of-the-art methods.
The framework effectively recognized motor imagery EEG signals.

Conclusions:

The proposed CDAN framework is effective for motor imagery EEG signal decoding.
This approach enhances brain-computer interface capabilities by enabling cross-subject adaptation.
The study highlights the potential of advanced deep learning for automatic perceptual decision decoding.