Search research articles

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

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

Band structure engineering of monolayer MoS₂ by surface ligand functionalization for enhanced photoelectrochemical hydrogen production activity.

Nanoscale·2014

Same author

Hippo signaling influences HNF4A and FOXA2 enhancer switching during hepatocyte differentiation.

Cell reports·2014

Same author

Enumeration, genetic characterization and antimicrobial susceptibility of Lactobacillus and Streptococcus isolates from retail yoghurt in Beijing, China.

Biomedical and environmental sciences : BES·2014

Same author

Pleiotropy of the Drosophila JAK pathway cytokine Unpaired 3 in development and aging.

Developmental biology·2014

Same author

Stackelberg game of buyback policy in supply chain with a risk-averse retailer and a risk-averse supplier based on CVaR.

PloS one·2014

Same author

Topological transport and atomic tunnelling-clustering dynamics for aged Cu-doped Bi2Te3 crystals.

Nature communications·2014

Same journal

A computational framework for fitting biophysical basal-ganglia network models, applied to Parkinsonian beta oscillations.

Journal of neural engineering·2026

Same journal

A sensor-driven Hill-type muscle modeling framework integrating sEMG and pFMG for biceps brachii force estimation.

Journal of neural engineering·2026

Same journal

Overcoming brain non-stationarity: Adaptive RLS classification for stable BCIs based on auditory evoked potentials.

Journal of neural engineering·2026

Same journal

Mapping neural representations of fine and gross upper-limb movements across dorsoventral subthalamic nucleus subregions in Parkinson's disease.

Journal of neural engineering·2026

Same journal

Ultra-flexible wireless endovascular stimulator for cortical simulation.

Journal of neural engineering·2026

Same journal

Influence of frequency and pulse train duration on respiratory responses during transcutaneous phrenic nerve stimulation in humans.

Journal of neural engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 8, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG

Ketong Li¹, Peng Chen¹, Qian Chen¹

¹School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, People's Republic of China.

Journal of Neural Engineering

|December 24, 2024

Summary

This summary is machine-generated.

This study introduces a novel hybrid AI network for decoding electroencephalogram (EEG) signals, improving brain-computer interface (BCI) performance for clinical use. The transformer-based model enhances motor imagery decoding accuracy and practical applications.

Keywords:

EEG classification convolutional neural network cross attention modified locally linear embedding transformer

More Related Videos

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: May 8, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Brain-computer interfaces (BCIs) utilize artificial intelligence (AI) for electroencephalogram (EEG) signal decoding, offering a new human-machine interaction method.
Current EEG decoding methods lack sufficient performance for clinical applications due to incomplete information extraction and limited computational resources.

Purpose of the Study:

To introduce a hybrid AI network for enhanced EEG decoding in BCIs.
To address limitations in current EEG decoding for clinical settings by improving information extraction and computational efficiency.

Main Methods:

A hybrid network combining a transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
Separate extraction and cross-attention fusion of channel and temporal features from EEG signals.
Application of manifold learning for dimensionality reduction to decrease computational burden.

Main Results:

Achieved high accuracy rates: 84.44% (BCI Competition IV dataset 2a), 94.96% (high gamma dataset), and 82.79% (self-constructed motor imagery dataset).
Outperformed baseline models using EEG-channel transformer with dimension-reduced EEG data and window attention with sliding window convolution.
Visualizations demonstrated the model's preference for task-related channels, enhancing interpretability.

Conclusions:

The proposed transformer-based method significantly improves motor imagery EEG decoding.
This approach enhances the practicality of BCI technology for future clinical applications.