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

ZNF146 accelerates lung adenocarcinoma progression through MDM2/p53 and PHGDH/ferroptosis.

Cell & bioscience·2025
Same author

Intelligent Gene Delivery System Functionalized Metal Implants for Fracture Repair via Remodeling Mitochondrial Homeostasis.

Advanced healthcare materials·2025
Same author

Montmorillonite-Based Oral Vaccine for Colorectal Cancer Immunotherapy through Mucosal Immune Activation.

Journal of the American Chemical Society·2025
Same author

Baicalein alleviates chronic acute stress-induced irritable bowel syndrome-like symptoms in rats via modulating the ODC1/NF-κB pathway and oxidative stress.

Biochemical and biophysical research communications·2025
Same author

A Single-Cell Atlas-Inspired Hitchhiking Therapeutic Strategy for Acute Pancreatitis by Restricting ROS in Neutrophils.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Transcription factor ZNF266 suppresses cancer progression by modulating CA9-mediated intracellular pH alteration in lung adenocarcinoma.

Respiratory research·2025
Same journal

Statement of Retraction: MicroRNA miR-331-3p suppresses osteosarcoma progression via the Bcl-2/Bax and Wnt/β-Catenin signaling pathways and the epithelial-mesenchymal transition by targeting N-acetylglucosaminyltransferase I (MGAT1).

Bioengineered·2026
Same journal

Statement of Retraction: Easy efficient HDR-based targeted knock-in in <i>Saccharomyces cerevisiae</i> genome using CRISPR-Cas9 system.

Bioengineered·2026
Same journal

Statement of Retraction: Geniposide promotes splenic Treg differentiation to alleviate colonic inflammation and intestinal barrier injury in ulcerative colitis mice.

Bioengineered·2026
Same journal

Statement of Retraction: Circular RNA hsa_circ_0000437 may be used as a new indicator for the diagnosis and prognosis of hepatocellular carcinoma.

Bioengineered·2026
Same journal

Statement of Retraction: The role of autophagy-related proteins in the pathogenesis of neuromyelitis optica spectrum disorders.

Bioengineered·2026
Same journal

Statement of Retraction: Long non-coding RNA lincRNA-erythroid prosurvival attenuates inflammation by enhancing myosin heavy chain 6 stability through recruitment of heterogeneous nuclear ribonucleoprotein L in myocardial infarction.

Bioengineered·2026
See all related articles

Related Experiment Video

Updated: Apr 7, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.2K

Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.

Xiaoguo Ying1, Han Lin1, Guohua Hui1

  • 1a College of Information Engineering; Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province ; Zhejiang A & F University ; Linan , China.

Bioengineered
|July 16, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for Electroencephalogram (EEG) signal analysis using a non-linear bistable dynamical model. The technique effectively extracts key features from EEG data, improving signal discrimination.

Keywords:
EEG analysisbistable dynamics modelcoherence indexdiscriminationnon-linear

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

22.1K
Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

16.0K

Related Experiment Videos

Last Updated: Apr 7, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.2K
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

22.1K
Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

16.0K

Area of Science:

  • Neuroscience
  • Signal Processing
  • Dynamical Systems

Background:

  • Electroencephalogram (EEG) records brain's electrical activity via scalp voltage fluctuations.
  • EEG signals, derived from neuronal ionic currents, are crucial for future research.
  • Analyzing complex EEG signals requires advanced computational models.

Purpose of the Study:

  • To propose a novel method for EEG signal discrimination.
  • To utilize a non-linear bistable dynamical model for EEG analysis.
  • To characterize EEG signal features using a coherence index.

Main Methods:

  • Processing of EEG signals using a non-linear bistable dynamical model.
  • Characterization of extracted EEG signal features via coherence index.
  • Experimental validation of the proposed signal discrimination approach.

Main Results:

  • The non-linear bistable dynamical model successfully processed EEG signals.
  • The coherence index effectively characterized distinct EEG signal features.
  • The proposed method demonstrated accurate feature extraction for different EEG signals.

Conclusions:

  • The non-linear bistable dynamical model offers a robust approach for EEG signal analysis.
  • This method enhances the ability to discriminate between different types of EEG signals.
  • The findings support the significance of EEG signal analysis in neuroscience and related fields.