Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Dec 20, 2025

Preterm EEG: A Multimodal Neurophysiological Protocol

Preterm EEG: A Multimodal Neurophysiological Protocol

Published on: February 18, 2012

Single-Trial EEG Responses Classified Using Latency Features.

Irzam Hardiansyah¹, Valentina Pergher^2,3, Marc M Van Hulle³

¹Department of Computer Science, KU Leuven - University of Leuven, Celestijnenlaan 200A, P.O. Box 2402, 3000 Leuven, Belgium.

International Journal of Neural Systems

|June 4, 2020

Summary

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

Correction: EEG-based classification of alzheimer's disease and frontotemporal dementia using functional connectivity.

Scientific reports·2026

Same author

The electrophysiological basis of resting-state fMRI hyperconnectivity in early Alzheimer's disease.

Alzheimer's research & therapy·2026

Same author

Functional connectivity in infants' visual cortex and its links to motion processing and autism.

Scientific reports·2026

Same author

A Cross-Subject Band-Power Complexity Metric for Detecting Mental Fatigue Through EEG.

Brain sciences·2026

Same author

Word classification across speech modes from low-density electrocorticography signals.

Journal of neural engineering·2026

Same author

EEG-based classification of alzheimer's disease and frontotemporal dementia using functional connectivity.

Scientific reports·2026

This study on covert attention training in older adults found that latency-based EEG analysis improved classification accuracy. This suggests brain plasticity and offers new insights into attention and learning.

Area of Science:

Neuroscience
Cognitive Science
Machine Learning in Neuroscience

Background:

Covert attention influences electroencephalography (EEG) responses following practice.
Machine learning (ML) for single-trial EEG classification often prioritizes amplitude over latency features.

Purpose of the Study:

To investigate changes in EEG response signatures during 10 sessions of covert attention training in healthy older adults.
To compare the efficacy of latency-based versus amplitude-based features for ML classification of EEG patterns.

Main Methods:

Nine healthy older adults underwent 10 sessions of covert attention training.
EEG data were recorded and analyzed using ML classifiers distinguishing between target stimulus presence and absence.
Classifiers were trained using both latency-based and amplitude-based EEG features.

Keywords:

EEG Longitudinal covert attention training latency features machine learning classification

More Related Videos

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

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

Published on: May 25, 2019

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Published on: September 1, 2018

Related Experiment Videos

Last Updated: Dec 20, 2025

Preterm EEG: A Multimodal Neurophysiological Protocol

Preterm EEG: A Multimodal Neurophysiological Protocol

Published on: February 18, 2012

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

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

Published on: May 25, 2019

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Published on: September 1, 2018

Main Results:

Latency-based ML classifiers significantly outperformed amplitude-based classifiers in distinguishing EEG patterns.
Classification accuracy improved concurrently with behavioral accuracy across training sessions.
These findings provide evidence for neuroplasticity induced by attention training.

Conclusions:

Latency-based features are crucial for accurately classifying single-trial EEG responses in the context of attention training.
Covert attention training in older adults enhances EEG signatures, demonstrating brain plasticity.
This research highlights the potential of ML and EEG for understanding cognitive training effects.