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

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

13.6K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
13.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Distinct rhizosphere microbiomes and metabolomes mediate Fusarium crown rot resistance across wheat cultivars.

NPJ biofilms and microbiomes·2026
Same author

Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.

Frontiers in neuroscience·2026
Same author

Speed-Sensitive EEG Biomarkers in a Motion Tracking Paradigm: Implications for Dynamic Visual Acuity Research.

Brain sciences·2026
Same author

Improving Individual-Specific SSVEP-BCI with Adaptive Channel and Subspace Selection in TRCA.

Sensors (Basel, Switzerland)·2026
Same author

Adjuvant radiotherapy and survival in patients with malignant phyllode tumors: a population-based retrospective study.

Breast cancer (Tokyo, Japan)·2025
Same author

Research progress on self-heating effects and thermal management strategies of GaN-based electronic devices.

Journal of physics. Condensed matter : an Institute of Physics journal·2025

Related Experiment Video

Updated: May 1, 2026

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

21.2K

Channel component correlation analysis for multi-channel EEG feature component extraction.

Wenqiang Yan1,2, Qi Luo1, Chenghang Du1

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Frontiers in Neuroscience
|April 17, 2025
PubMed
Summary

A new channel component correlation analysis (CCCA) method effectively extracts features from multi-channel electroencephalogram (EEG) signals. This approach offers improved performance over traditional PCA and ICA for brain-computer interface and disease diagnosis applications.

Keywords:
channel component correlation analysiselectroencephalogram (EEG)event-related potentials (ERP)feature component extractionmulti-channel signal

More Related Videos

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.0K
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

5.6K

Related Experiment Videos

Last Updated: May 1, 2026

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

21.2K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.0K
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

5.6K

Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG) signal analysis is crucial for brain disease diagnosis, neuromodulation, and brain-computer interfaces (BCI).
  • EEG signals are complex due to non-stationarity, non-linearity, and noise, posing challenges for traditional analysis methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA).
  • Existing methods like PCA and ICA have limitations in performance and computational efficiency for multi-channel EEG feature extraction.

Purpose of the Study:

  • To propose a novel Channel Component Correlation Analysis (CCCA) method for extracting feature components from multi-channel EEG signals.
  • To enhance the accuracy and effectiveness of EEG signal processing for various applications.
  • To address the limitations of existing component extraction techniques in EEG analysis.

Main Methods:

  • The study employed Empirical Wavelet Transform (EWT) to decompose multi-channel EEG signals into distinct frequency bands.
  • Reconstructed signals were used to build a multi-dimensional signal representation.
  • An objective optimization function was designed to maximize covariance, and CCCA was utilized to extract feature components via calculated weight coefficients.

Main Results:

  • The CCCA method successfully identified the most relevant frequency bands within multi-channel EEG data.
  • CCCA demonstrated superior effectiveness in extracting common components compared to PCA and ICA.
  • The findings highlight the significance of CCCA for enhancing the accuracy of EEG analysis.

Conclusions:

  • The proposed CCCA method exhibits excellent performance in feature component extraction for multi-channel EEG.
  • CCCA offers a promising approach for practical engineering applications in EEG analysis.
  • This method contributes to more effective processing of complex EEG data for improved diagnostic and BCI applications.