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

EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Breaking the Depth Barrier in Motor Imagery Classification via a Residual Depthwise-Separable Network.

IEEE transactions on cybernetics·2026
Same author

MICU1 deficiency exacerbates cisplatin-induced acute kidney injury by tubular apoptosis and mitochondrial dysfunction.

Free radical biology & medicine·2026
Same author

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks With Pyramid Squeeze Attention.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.

IEEE journal of biomedical and health informatics·2026
Same author

Identification and functional validation of mitochondria-related genes associated with tubular injury in kidney transplantation and ischemia-reperfusion injury.

Biology direct·2026

Related Experiment Video

Updated: Jan 14, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

586

RUNet: A Zero-Calibration Framework for Cross-Domain EEG Decoding via Riemannian and Unsupervised Representation

Jing Jin, Chongfeng Wang, Ren Xu

    IEEE Transactions on Bio-Medical Engineering
    |January 12, 2026
    PubMed
    Summary

    RUNet, a novel zero-calibration framework, enhances brain-computer interface (BCI) performance by reducing variability in electroencephalography (EEG) signals. This approach achieves high accuracy in motor imagery decoding across sessions and subjects.

    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

    44.0K
    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

    15.2K

    Related Experiment Videos

    Last Updated: Jan 14, 2026

    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    586
    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

    44.0K
    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

    15.2K

    Area of Science:

    • Neuroscience
    • Machine Learning
    • Biomedical Engineering

    Background:

    • Variability in electroencephalography (EEG) signals presents a significant hurdle for reliable neural decoding in brain-computer interface (BCI) applications.
    • Individual differences and environmental factors contribute to inter-session and inter-subject variability, complicating BCI system development.

    Purpose of the Study:

    • To introduce RUNet, a zero-calibration framework designed to overcome EEG signal variability for improved motor imagery decoding in BCIs.
    • To develop a robust method for unsupervised representation learning and Riemannian manifold learning in EEG data.

    Main Methods:

    • RUNet employs a multi-scale spatiotemporal convolutional module for capturing complex EEG dynamics.
    • A polysynergistic covariance optimization module enhances feature robustness against non-stationarity.
    • Integration of Riemannian Affine Log Mapping mitigates cross-domain covariance drift, promoting domain-invariant feature learning.
    • A transfer learning framework with unsupervised contrastive pre-training and task-specific retraining is utilized.

    Main Results:

    • RUNet achieved high average cross-session accuracies (87.19%, 88.03%, 85.45%) on multiple datasets.
    • Cross-subject accuracies reached up to 87.25% on laboratory data and 78.14% on the PhysioNet dataset.
    • Demonstrated effective cross-domain generalization capabilities.

    Conclusions:

    • RUNet's unified pipeline effectively addresses EEG variability challenges in BCI.
    • The framework shows robust performance and generalization across different datasets and conditions.
    • RUNet offers a promising solution for practical, zero-calibration BCI applications.