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 Experiment Video

Updated: Jun 5, 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

Time-varying model identification for time-frequency feature extraction from EEG data.

Yang Li1, Hua-Liang Wei, Stephen A Billings

  • 1Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapping Street, Sheffield S1 3JD, UK. coq08yl@sheffield.ac.uk

Journal of Neuroscience Methods
|December 28, 2010
PubMed
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

EEG Signal Processing Techniques and Applications-2nd Edition.

Sensors (Basel, Switzerland)·2025
Same author

Editorial: New theories, models, and AI methods of brain dynamics, brain decoding and neuromodulation.

Frontiers in neuroscience·2023
Same author

SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking.

Sensors (Basel, Switzerland)·2022
Same author

Hierarchically Hybrid Porous Co<sub>3</sub>O<sub>4</sub>@NiMoO<sub>4</sub>/CoMoO<sub>4</sub> Heterostructures for High-Performance Electrochemical Energy Storage.

ACS applied materials & interfaces·2022
Same author

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Human brain mapping·2021
Same author

FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model.

Sensors (Basel, Switzerland)·2021
Same journal

Detection of cochlear microphonic for differential diagnosis between auditory neuropathy mice and noise-induced sensorineural hearing loss mice.

Journal of neuroscience methods·2026
Same journal

Assessment metrics for pain control in rats: A methodological commentary.

Journal of neuroscience methods·2026
Same journal

Infant EEG preprocessing pipelines: A capability framework and current gaps in practice.

Journal of neuroscience methods·2026
Same journal

Methods for measuring neural activity during voluntary wheel running.

Journal of neuroscience methods·2026
Same journal

Serotype-dependent differences in AAV cellular transduction rates in the hypothalamus of Arctic ground squirrels.

Journal of neuroscience methods·2026
Same journal

Rapid generation of human sensory neurons from iPSC for modeling of peripheral neuropathies.

Journal of neuroscience methods·2026
See all related articles

This study introduces a new multi-wavelet based time-varying AutoRegressive with eXogenous input (TVARX) model. The novel approach effectively tracks signal changes and captures transient dynamics in nonstationary processes.

Area of Science:

  • Signal Processing
  • Time Series Analysis
  • Biomedical Engineering

Background:

  • Nonstationary signals present challenges in accurate modeling due to their evolving properties.
  • Traditional AutoRegressive models struggle to capture rapid changes in signal dynamics.
  • Estimating time-varying parameters is crucial for understanding dynamic systems.

Purpose of the Study:

  • To investigate a novel modeling scheme for estimating and tracking time-varying properties of nonstationary signals.
  • To introduce a time-varying AutoRegressive with eXogenous input (TVARX) model utilizing multi-wavelet basis functions.
  • To demonstrate the capability of the proposed model in capturing both smooth trends and sharp changes.

Main Methods:

  • Development of a time-varying AutoRegressive with eXogenous input (TVARX) model.

Related Experiment Videos

Last Updated: Jun 5, 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

  • Representation of time-varying parameters using multi-wavelet basis functions.
  • Application of the orthogonal least square (OLS) algorithm for parameter estimation refinement.

    • Main Results:

    • The multi-wavelet approach effectively tracks smooth trends in nonstationary signals.
    • The scheme successfully captures sharp changes in time-varying process parameters.
    • Simulation studies and real EEG data analysis confirm the algorithm's efficacy.

    Conclusions:

    • The proposed multi-wavelet TVARX model provides a robust method for analyzing nonstationary signals.
    • The algorithm offers valuable transient information on the inherent dynamics of complex processes.
    • This approach enhances the understanding of dynamic systems in fields like neuroscience.