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

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Sensors (Basel, Switzerland)·2024
Same author

Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation.

IEEE open journal of engineering in medicine and biology·2022
Same author

Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture.

Sensors (Basel, Switzerland)·2022
Same author

Detection of Collaterals from Cone-Beam CT Images in Stroke.

Sensors (Basel, Switzerland)·2021
Same author

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.

Sensors (Basel, Switzerland)·2021
Same author

Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model.

Sensors (Basel, Switzerland)·2021
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

IEEE journal of biomedical and health informatics·2026
Same journal

PGCASurv: A Prior-Guided Cross-Attention Framework for Dynamic Survival Model with Longitudinal Data.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Apr 21, 2026

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.6K

Single-Trial Visual Evoked Potential Extraction Using Partial Least-Squares-Based Approach.

Duma Kristina Yanti, Mohd Zuki Yusoff, Vijanth Sagayan Asirvadam

    IEEE Journal of Biomedical and Health Informatics
    |November 7, 2014
    PubMed
    Summary
    This summary is machine-generated.

    A new partial least-squares (PLS) method accurately extracts single-trial visual evoked potential (VEP) signals from electroencephalography (EEG) data. This technique improves VEP analysis, reducing patient fatigue and aiding brain-computer interface applications.

    More Related Videos

    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

    9.1K
    Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
    09:36

    Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

    Published on: May 12, 2014

    14.4K

    Related Experiment Videos

    Last Updated: Apr 21, 2026

    Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
    09:42

    Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

    Published on: May 12, 2019

    6.6K
    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

    9.1K
    Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
    09:36

    Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

    Published on: May 12, 2014

    14.4K

    Area of Science:

    • Neuroscience
    • Signal Processing
    • Biomedical Engineering

    Background:

    • Visual evoked potential (VEP) analysis is crucial for diagnosing visual pathway disorders.
    • Traditional VEP analysis requires multiple trials, increasing test duration and patient discomfort.
    • Extracting single-trial VEPs from electroencephalography (EEG) signals presents significant challenges due to low signal-to-noise ratios.

    Purpose of the Study:

    • To develop and evaluate a novel partial least-squares (PLS) regression method for single-trial VEP signal extraction.
    • To estimate the latencies of key VEP components (P100, P200, P300, N75, N135) from both artificial and real EEG data.
    • To compare the performance of the PLS method against existing techniques like generalized eigenvalue decomposition (GEVD).

    Main Methods:

    • Application of partial least-squares (PLS) regression for single-trial VEP signal extraction.
    • Simulation using artificial EEG signals to assess latency error rates for multiple VEP peaks.
    • Analysis of real EEG signals from hospital data, focusing on P100 peak detection and standard deviation.
    • Comparison with the generalized eigenvalue decomposition (GEVD) algorithm, which utilizes prestimulation EEG data.

    Main Results:

    • The PLS algorithm successfully reconstructed artificial EEG signals into ideal VEP shapes.
    • PLS demonstrated comparable performance to GEVD for the P100 component.
    • PLS showed superior performance for later VEP peaks (P200, P300) and the N75 component.
    • GEVD provided better estimation for the N135 component, while PLS did not alter the input EEG signal.

    Conclusions:

    • The proposed PLS method offers a viable approach for single-trial VEP extraction without altering the input EEG signal.
    • PLS provides results comparable to GEVD and offers advantages for later VEP peaks.
    • This technique has potential applications in reducing patient fatigue, enhancing brain-computer interfaces (BCI), and improving EEG-fMRI integration.