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

Cryogenic silicification enables nongenetic functional continuity across mammalian cell generations.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

HPV Infection Drives CXCL8<sup>+</sup> Neutrophil-Mediated Immune Suppression in Cervical Cancer.

BioFactors (Oxford, England)·2026

Same author

Decreased TXNRD1 is associated with resistance to tagraxofusp in blastic plasmacytoid dendritic cell neoplasms, as seen in phase II.

Leukemia·2026

Same author

Harnessing Silicene-to-Silicic Acid Conversion for Organelle-Specific Silica Deposition in Tumor Therapy.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

The evolving clinical profile of laryngeal tuberculosis: a 10-year retrospective study from a tertiary hospital in northern China.

BMC infectious diseases·2026

Same author

Metal Recycling and Re-Utilization Strategies of E-Wastes in Catalysis Applications: A Mini Review.

Chemistry, an Asian journal·2026

Search research articles

Related Experiment Video

Updated: May 10, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

ADHD detection from EEG signals using GCN based on multi-domain features.

Ling Li¹, Xueyang Guo¹, Zihan Yang¹

¹College of Communication Engineering, Jilin University, Changchun, China.

Frontiers in Neuroscience

|April 21, 2025

Summary

This study introduces a novel graph convolutional neural network (GCN) approach for detecting attention deficit hyperactivity disorder (ADHD) using electroencephalogram (EEG) data, achieving high accuracy. The method combines time, frequency, and connectivity features for improved ADHD diagnosis.

Keywords:

ADHD EEG GCN brain functional connectivity multi-domain features

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

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Related Experiment Videos

Last Updated: May 10, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

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

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Neuroscience
Computational Psychiatry
Machine Learning

Background:

Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood psychiatric condition.
Accurate and rapid automated detection of ADHD using electroencephalogram (EEG) data remains a challenge.

Purpose of the Study:

To develop a novel graph convolutional neural network (GCN)-based framework for ADHD detection.
To leverage multi-domain EEG features, including time-domain, frequency-domain, and functional connectivity patterns.
To enhance the accuracy and reliability of automated ADHD diagnosis.

Main Methods:

Extracted time-domain and frequency-domain EEG features using Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models.
Constructed a functional connectivity matrix by fusing Phase Lag Index (PLI) and Coherence (COH) features.
Integrated extracted features and connectivity patterns into a GCN model for ADHD classification.

Main Results:

Achieved high average accuracies of 97.29% and 96.67% on two independent EEG datasets.
Outperformed traditional machine learning models including XGBoost, LightGBM, AdaBoost, and random forest.
Visualization revealed distinct brain connectivity patterns differentiating ADHD patients from healthy controls.

Conclusions:

The proposed GCN framework effectively integrates multi-domain EEG features for precise ADHD detection.
The fused functional connectivity matrix offers a more comprehensive characterization of brain interactions than single-metric approaches.
This method shows potential for clinical diagnostic assistance and provides neurophysiological insights into ADHD.