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

Cognitive Strategy-based neuromodulation optimizes neural communication to improve working memory.

NeuroImage·2026
Same author

Integrating Pharmacovigilance Data Mining and Mendelian Randomization to Identify Risk Profiles and Causal Targets of Opioid-Induced Delirium.

CNS neuroscience & therapeutics·2026
Same author

Electrocatalytic valorization of waste sulfur-containing species.

Chemical communications (Cambridge, England)·2026
Same author

Dietary Pineapple Pomace Complex Improves Growth Performance and Reduces Fecal Odor in Weaned Piglets by Modulating Fecal Microbiota, SCFAs, and Indoles.

Animals : an open access journal from MDPI·2025
Same author

Scalable ruthenium core-shell hydrogen catalyst for efficient and robust proton-exchange membrane electrolyser.

Nature materials·2025
Same author

Mettl1-mediated m<sup>7</sup>G modification of Fgfr2 regulates osteogenic and chondrogenic differentiation of mesenchymal stem cells.

International journal of biological sciences·2025

Related Experiment Video

Updated: Sep 13, 2025

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

Interpretable Cross-Modal Alignment Network for EEG Visual Decoding With Algorithm Unrolling.

Daowen Xiong, Liangliang Hu, Jiahao Jin

    IEEE Transactions on Neural Networks and Learning Systems
    |July 30, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel network for decoding electroencephalography (EEG) signals from visual stimuli, improving accuracy and reducing data needs. The method enhances object recognition in novel classes using cross-modal alignment and a new EEG encoder, ISTANet.

    More Related Videos

    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

    14.8K
    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

    Related Experiment Videos

    Last Updated: Sep 13, 2025

    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
    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

    14.8K
    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

    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Accurate electroencephalography (EEG) decoding for rapid visual stimuli is difficult due to low signal-to-noise ratio (SNR).
    • Existing neural networks face challenges in generalization and interpretability for EEG data.
    • Current methods often require extensive neural data for training decoders.

    Purpose of the Study:

    • To propose a cross-modal aligned network, E2IVAE, for enhanced EEG decoding of visual perceptual information.
    • To introduce ISTANet, a novel EEG encoder based on algorithm unrolling, for improved accuracy and stability.
    • To reduce the amount of neural data needed for training neural decoders.

    Main Methods:

    • Developed E2IVAE, a cross-modal aligned network leveraging shared information from multiple modalities.
    • Introduced ISTANet, an EEG encoder using algorithm unrolling for end-to-end feature extraction from noisy EEG signals.
    • Integrated interpretability of traditional machine learning with deep learning approaches.

    Main Results:

    • Achieved state-of-the-art (SOTA) top-1 accuracy of 62.39% and top-5 accuracy of 88.98% on a 200-class zero-shot neural decoding task.
    • Demonstrated robust performance and generalization on a challenging large-scale RSVP dataset, significantly above chance-level.
    • Enabled visualization and analysis of multiscale atom and reconstruction features, exploring biological plausibility.

    Conclusions:

    • The proposed E2IVAE framework with ISTANet significantly enhances EEG decoding accuracy and stability for object recognition in novel classes.
    • The method reduces the need for extensive training data while maintaining high performance.
    • This research offers critical insights into neural decoding, brain-computer interfaces (BCIs), cognitive science, and artificial intelligence.