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

Search research articles

Related Experiment Video

Updated: Sep 30, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Auto-Denoising for EEG Signals Using Generative Adversarial Network.

Yang An¹, Hak Keung Lam², Sai Ho Ling¹

¹School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.

Sensors (Basel, Switzerland)

|March 10, 2022

Summary

This summary is machine-generated.

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

LiquidGAN for Handwriting-Based Detection and Severity Classification of Extrapyramidal Symptoms.

Sensors (Basel, Switzerland)·2026

Same author

Vertebra-Level Completeness Analysis in Thoracolumbar Ultrasound Using a YOLO-Based Detection Framework.

Sensors (Basel, Switzerland)·2026

Same author

Voice-Based Assessment of Extrapyramidal Symptoms Using Deep Learning.

Sensors (Basel, Switzerland)·2025

Same author

MUF-Net: A Novel Self-Attention Based Dual-Task Learning Approach for Automatic Left Ventricle Segmentation in Echocardiography.

Sensors (Basel, Switzerland)·2025

Same author

Age-Related Differences in the Choroid Plexus Structural Integrity Are Associated with Changes in Cognition.

medRxiv : the preprint server for health sciences·2025

Same author

Research progress on the mechanisms of microglial extracellular vesicles affecting the prognosis of ischemic stroke.

Neurochemistry international·2025

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

This study introduces an automatic generative adversarial network (GAN) for denoising electroencephalogram (EEG) signals, significantly reducing processing time while preserving signal integrity. The novel method achieves performance comparable to manual techniques, enabling efficient analysis of large EEG datasets.

Area of Science:

Neuroscience
Signal Processing
Artificial Intelligence

Background:

Brain-computer interfaces (BCIs) require effective electroencephalogram (EEG) signal denoising.
Manual denoising methods are time-consuming and may not scale for large datasets.

Purpose of the Study:

To propose an automatic generative adversarial network (GAN)-based method for denoising multichannel EEG signals.
To develop a novel loss function for preserving signal information and energy.
To introduce a new normalization technique (SETET) for stable GAN signal generation.

Main Methods:

A GAN-based model with an additional discriminator for noise assessment.
A novel loss function to retain signal information and energy.
Sample entropy threshold and energy threshold-based (SETET) normalization for signal stability.

Keywords:

brain–computer interface convolutional neural network denoising electroencephalogram generative adversarial network normalization

More Related Videos

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: Sep 30, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

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

Training on diverse subject data for generalizability.

Main Results:

The proposed GAN denoising network achieves performance comparable to manual hybrid artificial denoising methods.
The automatic denoising process significantly reduces processing time.
The model demonstrates generalizability to new subjects' data after training on diverse datasets.

Conclusions:

The developed GAN-based method offers an efficient and automatic solution for EEG signal denoising.
This approach is suitable for processing large volumes of EEG data in BCI research.
The method preserves essential signal characteristics while effectively removing noise.