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 18, 2026

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

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Using rapid visually evoked EEG activity for person identification.

Koel Das1, Sheng Zhang, Barry Giesbrecht

  • 1Department of Psychology and Institute for Collaborative Biotechnology, University of California, Santa Barbara, CA 93106, USA. das@psych.ucsb.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 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

Beyond detection rate: understanding the vigilance decrement using signal detection theory.

Frontiers in cognition·2026
Same author

Predicting attentional lapses using response time speed in continuous performance tasks.

Frontiers in cognition·2026
Same author

Distinct roles of central and peripheral vision in rapid scene understanding.

Journal of vision·2026
Same author

Learned foveal representations, not gaze-mediated information access, underlie perceptual advantages for canonical face configurations.

Journal of vision·2026
Same author

Value-driven attentional capture prevents commission errors in real time.

Psychonomic bulletin & review·2026
Same author

Visual exposure to masked faces benefits personally familiar but not famous face recognition.

Frontiers in psychology·2026
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

Electroencephalography (EEG) recordings during visual tasks can identify individuals. Machine learning identified unique neural patterns in the visual cortex within 120-200 ms for person identification.

Area of Science:

  • Neuroscience
  • Machine Learning
  • Biometrics

Background:

  • Person identification is crucial for security and personalized experiences.
  • Existing biometric methods often rely on physical traits or explicit user input.
  • Exploring novel methods like brainwave analysis for identification is an active research area.

Purpose of the Study:

  • To investigate the feasibility of using electroencephalography (EEG) for person identification.
  • To analyze neural activity patterns during a rapid visual categorization task for discriminative features.
  • To apply advanced machine learning techniques for holistic analysis of EEG data.

Main Methods:

  • EEG recordings were obtained from observers performing a rapid visual categorization task.

More Related Videos

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
10:41

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Published on: May 26, 2018

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

Related Experiment Videos

Last Updated: Jun 18, 2026

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

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
10:41

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Published on: May 26, 2018

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

  • Machine learning algorithms were employed to analyze a 0.5-second epoch of EEG data.
  • Discriminative spatio-temporal filters were extracted for holistic data analysis.

    • Main Results:

    • The analysis revealed sparse feature representations in both spatial and temporal domains.
    • Discriminative neural activity was localized to occipital electrodes (visual cortex).
    • Key temporal features were identified within the 120-200 ms interval post-stimulus.

    Conclusions:

    • EEG recordings during challenging perceptual tasks show potential for reliable person identification.
    • Spatio-temporal filter analysis provides insights into the neural basis of individual discrimination.
    • This approach offers a non-invasive and potentially robust biometric solution.