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

Factors of crisis and culture in international and Chinese death education research: a comparative bibliometric analysis.

Frontiers in medicine·2026
Same author

Comparative efficacy of combined exercise and nutritional interventions for sarcopenia: A systematic review and network meta-analysis incorporating remote delivery models.

Archives of gerontology and geriatrics·2026
Same author

EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection.

Sensors (Basel, Switzerland)·2026
Same author

Moving objects detection based on tensor ring low rank decomposition.

Scientific reports·2025
Same author

Reinforcement Learning Decoding Method of Multi-User EEG Shared Information Based on Mutual Information Mechanism.

IEEE journal of biomedical and health informatics·2025
Same author

Low-Temperature Sealing Material Database and Optimization Prediction Based on AI and Machine Learning.

Polymers·2025

Related Experiment Video

Updated: May 17, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Selecting EEG components using time series analysis in brain death diagnosis.

Gen Hori1, Jianting Cao

  • 1Department of Business Administration, Asia University, Tokyo, 180-8629 Japan ; Lab. for Advanced Brain Signal Processing, BSI, RIKEN, Saitama, 351-0198 Japan.

Cognitive Neurodynamics
|November 2, 2012
PubMed
Summary

Independent Component Analysis (ICA) helps remove artifacts from electroencephalogram (EEG) signals. This study focuses on automatically selecting artifact-free EEG components for accurate brain death diagnosis in organ transplantation.

Keywords:
Brain death diagnosisIndependent component analysisSignal processingTime series analysisWayland test

More Related Videos

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex
09:00

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

Related Experiment Videos

Last Updated: May 17, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex
09:00

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Diagnosing brain death for organ transplantation requires a flat electroencephalogram (EEG) test.
  • Artifacts from power lines and electrocardiograms (ECG) can contaminate EEG signals, complicating diagnosis.
  • Independent Component Analysis (ICA) is a technique used to separate mixed signals.

Purpose of the Study:

  • To develop an automatic method for selecting relevant EEG components after ICA.
  • To improve the accuracy and reliability of brain death diagnosis using EEG.
  • To address the challenge of artifact contamination in flat EEG testing.

Main Methods:

  • Applied Independent Component Analysis (ICA) to electroencephalogram (EEG) channel data.
  • Separated EEG signals into distinct components, some representing brain activity, others artifacts.
  • Developed a time series analysis approach for automatic selection of artifact-free components.

Main Results:

  • Successfully separated EEG signals into brain activity and artifact components using ICA.
  • Demonstrated the potential for automatic component selection based on time series analysis.
  • The proposed method aids in identifying true flat EEG signals for brain death diagnosis.

Conclusions:

  • Automatic component selection using time series analysis is a valuable tool for artifact removal in EEG.
  • This method can enhance the accuracy of brain death diagnosis in organ transplantation.
  • ICA-based artifact removal improves the reliability of electroencephalogram interpretation.