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

State-dependent dual-site prefrontal TMS bidirectionally modulates working-memory accuracy.

Frontiers in human neuroscience·2026
Same author

Genome-wide identification and gene expression analysis of Formin homology 2 (FH2) genes in peanut.

BMC plant biology·2026
Same author

Tract-explainable and underexplained synchrony play complementary roles in the functional organization of the brain.

bioRxiv : the preprint server for biology·2026
Same author

Beyond variance: sensitivity-based dimensions in brain networks underlie individual differences in cognitive ability.

ArXiv·2026
Same author

Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness.

bioRxiv : the preprint server for biology·2026
Same author

Personalized whole-brain Ising models with heterogeneous nodes capture differences among brain regions.

NeuroImage·2026

Related Experiment Video

Updated: Jul 15, 2025

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

16.5K

Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency

Guang Ouyang1, Changsong Zhou2

  • 1Faculty of Education, The University of Hong Kong, Hong Kong.

Bioengineering (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

This study integrates multiple electroencephalography (EEG) features to better understand brain responses to stimuli. Combining temporal, frequency, and phase dynamics improves neural dynamic information extraction for cognitive research.

Keywords:
EEGERPmachine learningphase dynamicssingle trialstime-frequency analysis

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

33.8K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.5K

Related Experiment Videos

Last Updated: Jul 15, 2025

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

16.5K
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

33.8K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.5K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Machine Learning

Background:

  • Characterizing brain responses in electroencephalography (EEG) is complex due to spontaneous activity.
  • Brain responses can be defined as new activity or changes in ongoing activity.
  • Commonly studied features include temporal waveforms, time-frequency representations, and phase dynamics.

Purpose of the Study:

  • To provide systematic guidance on exploiting multifaceted features in neural cognitive research.
  • To demonstrate the complementary nature of different EEG features.
  • To integrate diverse neural dynamic information using machine learning.

Main Methods:

  • Utilized a visual oddball event-related potentials (ERPs) dataset from 200 participants.
  • Analyzed transient temporal waveforms, time-frequency representations, and phase dynamics.
  • Employed neural-network-based machine learning for feature integration.

Main Results:

  • Information from different EEG features (temporal, frequency, phase) is complementary.
  • Integrated features provide a more comprehensive understanding of neural dynamics.
  • Machine learning effectively exploits multifaceted neural dynamic information.

Conclusions:

  • Multifaceted feature integration enhances the exploitation of neural dynamic information.
  • This approach offers improved insights for basic and applied cognitive research.
  • Systematic guidance is provided for utilizing diverse EEG features effectively.