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

Mimicking Nature to Achieve Rapid Liquid Absorption of Material.

ACS applied materials & interfaces·2026
Same author

Light Intensity-Driven Bidirectional Photoresponse Vision Sensor for Autonomous Obstacle Avoidance System.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Amide linkage engineering in phthalocyanine-based covalent organic frameworks for enhanced photocatalytic CO<sub>2</sub> reduction.

Chemical communications (Cambridge, England)·2026
Same author

Addition of anti-CD38 mAb in newly diagnosed multiple myeloma: advancing toward quadruplet induction regimens.

Blood neoplasia·2026
Same author

Lens shape change is influenced by zonular anchorage and stretching mechanism.

Experimental eye research·2026
Same author

Global, regional, and national burdens of lower extremity peripheral arterial disease from 1990 to 2021 and projections to 2050: global burden of disease study 2021.

Frontiers in cardiovascular medicine·2025

Related Experiment Video

Updated: Apr 28, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.6K

Deep Learning Decoding of Steady-State Visual Evoked Potential (SSVEP) for Real-Time Mobile Brain-Computer

Hanzhen Zhang1, Chunjing Tao1

  • 1School of Engineering Medicine, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, China.

Brain Sciences
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

This review summarizes deep learning technologies for real-time mobile brain-computer interfaces (BCIs) using steady-state visually evoked potentials (SSVEPs). It highlights advancements for practical deployment, focusing on performance, efficiency, and robustness.

Keywords:
brain-computer interfacesdeep learningelectroencephalographysteady-state visual evoked potential

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
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

11.4K

Related Experiment Videos

Last Updated: Apr 28, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.6K
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
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

11.4K

Area of Science:

  • Neuroscience
  • Computer Science
  • Human-Computer Interaction

Background:

  • Steady-state visually evoked potential-based brain-computer interfaces (SSVEP-BCIs) offer high transfer rates for mobile applications.
  • Deep learning significantly enhances SSVEP decoding, especially for short signals and complex tasks.
  • The Tsinghua Benchmark dataset standardizes evaluation for deep learning SSVEP decoding.

Purpose of the Study:

  • To provide a comprehensive summary of deep learning decoding technologies for real-time mobile SSVEP-BCI applications.
  • To identify key technical developments focusing on real-time performance, low computational complexity, and robustness.
  • To bridge the gap between laboratory research and practical deployment of mobile BCIs.

Main Methods:

  • Comprehensive literature review of SSVEP deep learning decoding studies published since 2023.
  • Focus on studies utilizing the Tsinghua Benchmark dataset.
  • Analysis of techniques addressing real-time processing, resource constraints, and environmental robustness.

Main Results:

  • Key technologies for real-time mobile SSVEP decoding are summarized.
  • Analysis details how techniques tackle engineering challenges for mobile BCI implementation.
  • Identified advancements in processing speed, efficiency, and signal stability in mobile environments.

Conclusions:

  • This review offers a thorough overview of deep learning-based SSVEP decoding for mobile applications.
  • It establishes a technical foundation for advancing mobile BCIs towards real-world use.
  • The findings support the transition of SSVEP-BCI technology from controlled lab settings to practical, mobile applications.