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

Community detection framework based on 3D shape descriptors for tree species classification in point cloud data.

Scientific reports·2026
Same author

Meta-analysis of technologies for diabetes treatment: glycemic control, prediction, meal and physical activity detection.

Progress in biomedical engineering (Bristol, England)·2026
Same author

A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection.

Frontiers in neuroscience·2025
Same author

Infra-slow frequency oscillations propagating through multiple organs convey information on phase-specific timing for self-initiated actions.

Brain research·2025
Same author

Do Nursing Students Write More Polite Health Related Prompts in Chatbots Compared to Engineering Students?

Studies in health technology and informatics·2025
Same author

Efficient sleep apnea detection using single-lead ECG: A CNN-Transformer-LSTM approach.

Computers in biology and medicine·2025

Related Experiment Video

Updated: May 24, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

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

Published on: April 26, 2024

1.6K

Near-lossless EEG signal compression using a convolutional autoencoder: Case study for 256-channel binocular rivalry

Martin Kukrál1, Duc Thien Pham1, Josef Kohout1

  • 1Faculty of Applied Sciences, University of West Bohemia in Pilsen, Pilsen, 301 00, Czech Republic.

Computers in Biology and Medicine
|March 6, 2025
PubMed
Summary

This study introduces a novel compression method for electroencephalography (EEG) data using artificial neural networks. The technique offers significant data reduction while preserving signal integrity, crucial for large-scale brain activity analysis.

Keywords:
Artificial neural networksData compressionElectroencephalographyMachine learningNeuroinformatics

More Related Videos

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
How to Build a Dichoptic Presentation System That Includes an Eye Tracker
05:48

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

Published on: September 6, 2017

8.4K

Related Experiment Videos

Last Updated: May 24, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

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

Published on: April 26, 2024

1.6K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
How to Build a Dichoptic Presentation System That Includes an Eye Tracker
05:48

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

Published on: September 6, 2017

8.4K

Area of Science:

  • Neuroscience
  • Computer Science
  • Signal Processing

Background:

  • Electroencephalography (EEG) generates large datasets due to high sampling rates and multiple electrodes.
  • Storing and transmitting extensive EEG data presents significant challenges.
  • Specialized compression techniques are required for efficient EEG data management.

Purpose of the Study:

  • To develop a novel compression method for EEG data.
  • To achieve substantial data reduction while maintaining signal fidelity.
  • To create a flexible near-lossless compression scheme tailored for EEG.

Main Methods:

  • Utilized a convolutional autoencoder, a type of artificial neural network, for lossy compression.
  • Implemented lossless corrections based on a user-defined amplitude loss threshold.
  • Applied the method to a 256-channel binocular rivalry EEG dataset for validation.

Main Results:

  • The proposed method demonstrated substantial compression ratios.
  • Significant improvements in compression speed were observed compared to baseline methods.
  • The compression scheme proved to be flexible and near-lossless.

Conclusions:

  • The artificial neural network-based compression method is effective for large EEG datasets.
  • The technique offers a promising solution for efficient storage and transmission of brain activity data.
  • Further research into this compression approach is warranted.