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

Transient and selective effects of acute exercise intensity on response inhibition: an EEG study.

Frontiers in human neuroscience·2026
Same author

A mega-analysis of EEG-based frontal-midline theta neurofeedback reveals learning dynamics, individual variability, and response profiles.

NeuroImage·2026
Same author

Conflict resolution and response inhibition: A simultaneous EEG-EMG-pupillometry study.

Neuropsychologia·2025
Same author

brain2print AI powered web tool for creating 3D printable brain models.

Scientific reports·2025
Same author

Neural markers of error processing relate to task performance, but not to substance-related risks and problems and externalizing problems in adolescence and emerging adulthood.

Developmental cognitive neuroscience·2024
Same author

Shared Patterns of Cognitive Control Behavior and Electrophysiological Markers in Adolescence.

Journal of cognitive neuroscience·2024

Related Experiment Video

Updated: Apr 5, 2026

Transauricular Vagus Nerve Stimulation and Electroencephalographic Assessment in Disorders of Consciousness
04:04

Transauricular Vagus Nerve Stimulation and Electroencephalographic Assessment in Disorders of Consciousness

Published on: July 11, 2025

1.9K

Group-level component analyses of EEG: validation and evaluation.

Rene J Huster1, Sergey M Plis2, Vince D Calhoun3

  • 1Department of Psychology, University of Oslo Oslo, Norway ; The Mind Research Network Albuquerque, NM, USA.

Frontiers in Neuroscience
|August 19, 2015
PubMed
Summary
This summary is machine-generated.

Group-level analysis of electroencephalographic (EEG) data using temporal concatenation with independent component analysis (ICA) or SOBI effectively reconstructs neural sources. Spatial concatenation showed opposite performance, highlighting method-specific sensitivities in multi-subject EEG studies.

Keywords:
EEGICASOBIblind source separationgroup component analysismultisubject analysis

More Related Videos

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

12.0K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.5K

Related Experiment Videos

Last Updated: Apr 5, 2026

Transauricular Vagus Nerve Stimulation and Electroencephalographic Assessment in Disorders of Consciousness
04:04

Transauricular Vagus Nerve Stimulation and Electroencephalographic Assessment in Disorders of Consciousness

Published on: July 11, 2025

1.9K
Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

12.0K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.5K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Group-level component analysis is established for functional magnetic resonance imaging (fMRI) but nascent for electroencephalography (EEG).
  • Assessing EEG group analysis methods for sensitivity to topographic variability and loose time-locking is crucial.
  • Existing methods require validation for multi-subject EEG data decomposition.

Purpose of the Study:

  • To evaluate independent component analysis (ICA) and second order blind source identification (SOBI) for multi-subject EEG group analysis.
  • To investigate the impact of temporal versus spatial data concatenation on decomposition performance.
  • To assess method sensitivity to varying spatial, frequency, and time-locking profiles of simulated neural sources.

Main Methods:

  • Simulated EEG data with diverse source characteristics were analyzed.
  • Independent Component Analysis (ICA) and Second Order Blind Source Identification (SOBI) were applied.
  • Temporal and spatial concatenation strategies were compared for multi-subject data decomposition.

Main Results:

  • Temporal concatenation with ICA or SOBI successfully reconstructed sources with both strict and loose time-locking.
  • Performance degraded with topographical variability when using temporal concatenation.
  • Spatial concatenation exhibited complementary performance patterns, being less affected by time-locking but sensitive to topography.

Conclusions:

  • Group-level decomposition of EEG data is a valid and promising approach for inferring latent multi-subject data structures.
  • Temporal concatenation is effective for sources with varying time-locking, while spatial concatenation is better for topographic variations.
  • Further adaptations are needed to optimize methods for inter-subject and inter-trial variance in EEG.