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

Generative Fuzzy System for Sequence-to-Sequence Learning via Rule-Based Inference.

IEEE transactions on neural networks and learning systems·2025
Same author

Fuzzy Rule-Based Differentiable Representation Learning.

IEEE transactions on neural networks and learning systems·2025
Same author

SEFP: Structure-Based Enzyme Function Prediction.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Empowering Social Growth Through Virtual Reality-Based Intervention for Children With Attention-Deficit/Hyperactivity Disorder: 3-Arm Randomized Controlled Trial.

JMIR serious games·2024
Same author

HGLA: Biomolecular Interaction Prediction Based on Mixed High-Order Graph Convolution With Filter Network via LSTM and Channel Attention.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same author

MINDG: a drug-target interaction prediction method based on an integrated learning algorithm.

Bioinformatics (Oxford, England)·2024

Related Experiment Video

Updated: Sep 21, 2025

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

9.3K

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review.

Xiuyu Huang1, Shuang Liang2, Zengguang Li3

  • 1Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China.

Plos One
|June 3, 2022
PubMed
Summary
This summary is machine-generated.

Electroencephalogram-based brain-computer interfaces (EVE-BCI) utilizing vibrotactile stimuli show promise as an alternative to traditional methods. This review identifies key factors, feasibility evidence, and research gaps for developing more sophisticated EVE-BCI systems.

More Related Videos

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

2.5K
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.3K

Related Experiment Videos

Last Updated: Sep 21, 2025

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

9.3K
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

2.5K
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.3K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Electroencephalogram-based brain-computer interfaces (EVE-BCI) offer a novel approach using vibrotactile stimuli.
  • This method presents a potential alternative to conventional motor imagery and visual-based BCIs.
  • Understanding the critical factors influencing EVE-BCI performance is essential for its advancement.

Purpose of the Study:

  • To extract and summarize crucial aspects of EVE-BCI from existing literature.
  • To investigate the synthetic evidence supporting the feasibility of EVE-BCI.
  • To provide recommendations for future research and development in EVE-BCI.

Main Methods:

  • A comprehensive literature search was conducted across five major databases.
  • Key concepts including data collection, stimulation paradigms, signal processing, and performance metrics were extracted.
  • Analysis focused on identifying trends, gaps, and influential factors in EVE-BCI research.

Main Results:

  • Seventy-nine studies were included, primarily featuring EEG data from healthy individuals.
  • P300 and Steady-State Somatosensory Evoked Potential (SSSEP) are the dominant paradigms.
  • While vibration location is explored, other vibrotactile factors receive limited attention; temporal EEG features and linear models are common, with subject-dependent offline evaluations prevalent.
  • EVE-BCI accuracies consistently exceed chance levels across diverse populations.

Conclusions:

  • This review synthesizes current trends and identifies research gaps in EVE-BCI.
  • Recommendations are provided for optimizing EVE-BCI design and a checklist is proposed for clear research reporting.
  • The findings serve as a valuable reference for developing more advanced and practical EVE-BCI systems.