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: May 30, 2026

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Isolating visual evoked responses--comparing signal identification algorithms.

Tom J Wright¹, Josefin Nilsson, Carol Westall

¹Institute of Neuroscience and Physiology, Department of Clinical Neuroscience and Rehabilitation, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. thomas.wright@sickkids.ca

Journal of Clinical Neurophysiology : Official Publication of the American Electroencephalographic Society

|August 4, 2011

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

Renal and Cardiovascular Complications Following Type 2 Diabetes Mellitus in People With and Without HIV: Data from Cohort Study on Morbidity and HIV in Sweden (COSMOHS) between 2010-2024.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026

Same author

Late diagnosis of HIV persists in Sweden: Differing social determinants among migrant and non-migrant people with HIV.

HIV medicine·2026

Same author

Factors mediating and associated with immunological response in women living with HIV in Sweden: A nationwide register-based study.

HIV medicine·2025

Same author

Lakartidningen·2024

Same author

[The role of EEG in tomorrow's medicine].

Lakartidningen·2024

Same author

The capacity of neurological pupil index to predict the absence of somatosensory evoked potentials after cardiac arrest - An observational study.

Resuscitation plus·2024

Same journal

Surface EEG to Identify Cognitive Motor Dissociation After Acute Brain Injury.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2026

Same journal

The Physics and Physiology of Electrical Stimulation of the Brain.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2026

Same journal

Neurophysiology in Postanoxic Encephalopathy: Pathophysiology, Prognosis, and Treatment.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2026

Same journal

A Review on the Functional Connectivity of the Human Opercular Cortex.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2026

Same journal

Surgical Aspects of Opercular Epilepsies.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2026

Same journal

Cost-Effectiveness of EEG Monitoring in Hypoxic-Ischemic Brain Injury After Cardiac Arrest.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2026

See all related articles

New signal identification algorithms significantly improve the detection of visual evoked potentials (VEP) buried in noise. These methods enhance signal-to-noise ratio and reduce the number of trials needed for accurate VEP analysis.

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Visual evoked potentials (VEPs) are crucial for assessing visual pathway function.
Traditional VEP analysis often struggles with low signal-to-noise ratios (SNR).
Computational methods offer potential improvements in VEP signal detection.

Purpose of the Study:

To compare the efficacy of novel signal identification algorithms for VEP recording.
To evaluate algorithms based on their ability to detect VEP signals amidst noise.
To assess the impact of these algorithms on VEP isolation and SNR.

Main Methods:

VEPs were recorded with and without visual stimuli.
Four distinct algorithms were developed to identify stimulus-evoked signals and assign trial weights.

More Related Videos

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Published on: July 1, 2016

Related Experiment Videos

Last Updated: May 30, 2026

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Simultaneous Recording of Electroretinography and Visual Evoked Potentials in Anesthetized Rats

Published on: July 1, 2016

Algorithm performance was evaluated by VEP detection accuracy, final VEP SNR, and trials needed for significant detection.

Main Results:

All algorithms successfully identified VEP trials with higher confidence than noise-only trials (P < 0.01).
The isolated VEPs showed no significant difference in timing or amplitude compared to traditional ensemble averaging.
The top-performing algorithm achieved a ninefold increase in the SNR of the extracted VEP waveform.

Conclusions:

Computational signal identification algorithms demonstrably enhance VEP detection in noisy recordings.
Integration with weighted averaging reduces the number of trials required for reliable VEP analysis.
These findings support the use of advanced algorithms for more efficient and accurate VEP assessment.