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 Experiment Video

Updated: Jun 26, 2026

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

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

A generalized subspace approach for estimating visual evoked potentials.

Nidal Kamel1, Mohd Zuki Yusoff

  • 1Electrical and Electronic Engineering Department, Universiti Teknologi Petronas, 31750 Tronoh, Perak, Malaysia. nidalkamel@petronas.com.my

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
Summary
This summary is machine-generated.

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

Functional excitation-inhibition ratio for social anxiety analysis and severity assessment.

Frontiers in psychiatry·2024
Same author

Emotion brain network topology in healthy subjects following passive listening to different auditory stimuli.

PeerJ·2024
Same author

Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

Frontiers in psychiatry·2023
Same author

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

Frontiers in psychiatry·2023
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 journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

This study introduces a new method, generalized subspace approach (GSA), to extract visual evoked potentials (VEP) from noisy EEG data. GSA effectively isolates VEP signals for better analysis of visual pathway function.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Visual evoked potentials (VEP) are crucial for assessing visual pathway function.
  • Extracting VEP from electroencephalogram (EEG) is challenging due to significant noise.
  • Current single-trial VEP extraction methods require further refinement.

Purpose of the Study:

  • To propose and validate a novel single-trial signal subspace approach for VEP extraction.
  • To enhance the accuracy of VEP latency estimation, particularly for P100.
  • To compare the performance of the proposed method against existing techniques.

Main Methods:

  • A generalized eigendecomposition is applied to VEP and noise covariance matrices.
  • The method jointly diagonalizes matrices, avoiding a pre-whitening step.

More Related Videos

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats
10:30

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Published on: July 1, 2016

Related Experiment Videos

Last Updated: Jun 26, 2026

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

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats
10:30

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Published on: July 1, 2016

  • The corrupted VEP space is decomposed into signal and noise subspaces; noise is removed.

    • Main Results:

    • The generalized subspace approach (GSA) effectively isolates VEP signals from EEG.
    • GSA demonstrates validity and effectiveness in estimating P100 latencies from real-world data.
    • Performance comparison indicates GSA's potential advantages over the Third Order Correlation (TOC) method.

    Conclusions:

    • The proposed GSA offers a robust method for single-trial VEP extraction.
    • GSA facilitates more accurate objective assessment of visual pathway function.
    • This technique holds promise for clinical applications in visual neurophysiology.