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

Continuous Cuffless Blood Pressure Estimation Based on Fractional Order Derivatives via Gramian Angular Field Only Using Photoplethysmograms.

IET systems biology·2025
Same author

Principal Component Analysis Based Quaternion-Valued Medians for Non-Invasive Blood Glucose Estimation.

Sensors (Basel, Switzerland)·2025
Same author

AMFF-Net: An Effective 3D Object Detector Based on Attention and Multi-Scale Feature Fusion.

Sensors (Basel, Switzerland)·2023
Same author

Fusion of various optimisation based feature smoothing methods for wearable and non-invasive blood glucose estimation.

IET systems biology·2023
Same author

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms.

Sensors (Basel, Switzerland)·2023
Same author

Deep Learning-Based Synthesized View Quality Enhancement with DIBR Distortion Mask Prediction Using Synthetic Images.

Sensors (Basel, Switzerland)·2022
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

Related Experiment Video

Updated: Sep 18, 2025

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.3K

Multi-Party Verifiably Collaborative Encryption for Biomedical Signals via Singular Spectrum Analysis-Based Chaotic

Xiwen Zhang1, Jianfeng He1, Bingo Wing-Kuen Ling1

  • 1School of Information Engineering, Guangdong University of Technology, Guangzhou 511006, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a secure, collaborative encryption method for biomedical sensor data using singular spectrum analysis (SSA) and chaotic networks. The system ensures verifiable, multi-party data protection for nonlinear and non-stationary signals.

Keywords:
biomedical signal encryptionchaotic filter bank networksmulti-party collaborative encryptionsingular spectrum analysisverifiable decryption

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.9K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.3K

Related Experiment Videos

Last Updated: Sep 18, 2025

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.3K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.9K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.3K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cryptography

Background:

  • Biomedical sensors generate complex nonlinear and non-stationary signals.
  • Secure encryption is crucial for sensitive biomedical data.
  • Existing methods may not support collaborative and verifiable encryption.

Purpose of the Study:

  • To propose a multi-party verifiably collaborative system for encrypting biomedical signals.
  • To utilize singular spectrum analysis (SSA)-based chaotic networks for signal encryption.
  • To ensure secure and verifiable data handling in collaborative environments.

Main Methods:

  • Decomposition of raw biomedical signals into multiple components using SSA.
  • Encryption of decomposed components via chaotic filter bank networks.
  • Flexible design of SSA window length and chaotic network layers for multi-party collaboration.

Main Results:

  • The proposed system effectively encrypts nonlinear and non-stationary biomedical signals.
  • Computer numerical simulations demonstrate good encryption performance.
  • The system supports verifiably collaborative multi-party encryption.

Conclusions:

  • The SSA-based chaotic network system offers a robust solution for secure biomedical signal encryption.
  • The flexible design accommodates varying numbers of collaborators.
  • This approach enhances data security and verifiability in collaborative biomedical research.