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

Oscillatory Correlates of Habituation: EEG Evidence of Sustained Frontal Theta Activity to Food Cues.

Sensors (Basel, Switzerland)·2026
Same author

A novel backpropagation algorithm based on negated kurtosis loss for training shallow, convolutional, and deep neural networks.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

How to Evaluate Signal Quality of ear-ECG?

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

Headache-specific hyperexcitation sensitises and habituates on different time scales: An event related potential study of pattern-glare.

Neuroimage. Reports·2025
Same author

Impact of Pulmonary Vein Isolation on Atrial Fibrillation Organisation: Correlation of Intracardiac and Surface Electrocardiogram Measures.

Journal of cardiovascular electrophysiology·2025
Same author

Symmetric projection attractor reconstruction: Transcutaneous auricular vagus nerve stimulation for visually induced motion sickness.

Autonomic neuroscience : basic & clinical·2025
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Biometrics from brain electrical activity: a machine learning approach.

Ramaswamy Palaniappan1, Danilo P Mandic

  • 1Department of Computer Science, University of Essex, Colchester, Wivenhoe Park, UK. rpalan@essex.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 15, 2007
PubMed
Summary
This summary is machine-generated.

This study explores using brain electrical activity from visual evoked potentials (VEP) for individual identification. Enhanced gamma band analysis significantly improved biometric accuracy in simulations.

More Related Videos

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Related Experiment Videos

Last Updated: Jun 20, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Area of Science:

  • Neuroscience
  • Biometrics
  • Signal Processing

Background:

  • Individual identification is crucial for security and access control.
  • Brain electrical activity, specifically visual evoked potentials (VEP), offers a unique biological signal.
  • Previous research has explored VEP for biometrics, but improvements are needed.

Purpose of the Study:

  • To establish a robust framework for Visual Evoked Potential (VEP)-based biometrics.
  • To investigate the utility of gamma band energy features within VEP signals for identification.
  • To enhance VEP biometric system performance through advanced signal processing techniques.

Main Methods:

  • Utilizing increased bandwidth for VEP signal acquisition.
  • Implementing spatial averaging to improve signal quality.
  • Developing more robust power spectrum features from VEP signals.
  • Employing advanced classification algorithms for individual identification.

Main Results:

  • The proposed framework demonstrates improved VEP signal analysis.
  • Enhanced gamma band features show significant potential for biometric applications.
  • Simulation results on a large subject group confirm improved classification accuracy.
  • The study successfully unifies and extends previous findings.

Conclusions:

  • VEP-based biometrics, particularly utilizing gamma band features, is a viable method for individual identification.
  • The developed framework offers a more accurate and robust approach compared to prior methods.
  • Further research and validation are warranted for real-world deployment.