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

Early Detriment Analysis of First-Line Nivolumab plus Ipilimumab-Based Therapy in Patients with Metastatic Non-Small Cell Lung Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Same author

Acoustofluidic Biosensors.

Micromachines·2026

Same author

NINJ1 hypomethylation enhances endothelial dysfunction in brain arteriovenous malformations.

Brain : a journal of neurology·2026

Same author

Long-Term Clopidogrel Versus Aspirin Monotherapy After Drug-Eluting Stent Implantation: A Nationwide Real-World Comparative Study.

The American journal of cardiology·2026

Same author

A thin film-mediated omnidirectional spiral surface acoustic waves in combination with porous hydrogel microparticles for rapid immuno-biosensing.

Biosensors & bioelectronics·2026

Same author

Disease characteristics and treatment outcomes in patients with resected early-stage ALK-positive non-small cell lung cancer from the randomized ALINA trial.

Lung cancer (Amsterdam, Netherlands)·2026

Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 10, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Emotional EEG classification using connectivity features and convolutional neural networks.

Seong-Eun Moon¹, Chun-Jui Chen², Cho-Jui Hsieh³

¹School of Integrated Technology, Yonsei University, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society

|August 31, 2020

Summary

This summary is machine-generated.

This study introduces a new system using brain connectivity with convolutional neural networks (CNNs) for better user state recognition from electroencephalography (EEG) signals. This approach enhances classification accuracy by analyzing functional brain networks.

Keywords:

Brain connectivity Convolutional neural network (CNN)Electroencephalography (EEG)Emotion

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Related Experiment Videos

Last Updated: Dec 10, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Area of Science:

Neuroscience
Machine Learning
Signal Processing

Background:

Convolutional neural networks (CNNs) are commonly used for user state recognition via electroencephalography (EEG) signals.
Current methods often use high-dimensional raw EEG data, limiting the exploitation of crucial brain connectivity information.
Brain connectivity is vital for understanding functional brain networks and estimating user perceptual states.

Purpose of the Study:

To introduce and validate a novel classification system integrating brain connectivity with CNNs for improved user state analysis.
To explore the effectiveness of different connectivity measures in enhancing classification performance.
To develop data-driven methods for constructing connectivity matrices to maximize classification accuracy.

Main Methods:

A new classification system combining CNNs with brain connectivity measures was developed.
Three distinct types of connectivity measures were employed to assess their impact on classification.
Two data-driven approaches were proposed for constructing the brain connectivity matrix.

Main Results:

The proposed system demonstrated effectiveness in emotional video classification using EEG signals.
The integration of brain connectivity significantly improved upon traditional raw EEG data approaches.
Analysis showed a correlation between the concentration of brain connectivity and classification performance related to emotional content.

Conclusions:

Utilizing brain connectivity alongside CNNs offers a more effective approach for user state recognition from EEG.
The proposed data-driven methods for connectivity matrix construction can optimize classification performance.
This research highlights the importance of functional brain network analysis in interpreting EEG data for perceptual state estimation.