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

BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction.

IEEE transactions on neural networks and learning systems·2025
Same author

Automated Detection of Substance-Use Status and Related Information from Clinical Text.

Sensors (Basel, Switzerland)·2022
Same author

IEViT: An enhanced vision transformer architecture for chest X-ray image classification.

Computer methods and programs in biomedicine·2022
Same author

On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays.

Sensors (Basel, Switzerland)·2021
Same author

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning.

Sensors (Basel, Switzerland)·2020
Same author

DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices.

IEEE journal of biomedical and health informatics·2017
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

Related Experiment Video

Updated: Jan 9, 2026

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
13:32

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

26.7K

Human Intracranial EEG Biometric Identification.

Benjamin M Belay, Stamos Katsigiannis

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Intracranial electroencephalography (iEEG) shows promise for human biometrics, achieving 95.84% accuracy in same-session identification. This novel approach overcomes limitations of traditional EEG biometrics, demonstrating resilience to template aging.

    More Related Videos

    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

    34.3K
    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

    44.0K

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
    13:32

    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

    Published on: June 26, 2012

    26.7K
    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

    34.3K
    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

    44.0K

    Area of Science:

    • Neuroscience
    • Biometrics
    • Signal Processing

    Background:

    • Traditional electroencephalography (EEG) biometrics face challenges like template aging.
    • Intracranial electroencephalography (iEEG) offers a potential alternative for robust biometric identification.

    Purpose of the Study:

    • To investigate the feasibility of using iEEG signals for human biometric identification.
    • To develop and assess a processing pipeline for iEEG-based biometrics.
    • To evaluate the performance of iEEG biometrics in same-session and cross-session scenarios.

    Main Methods:

    • A comprehensive pipeline was developed to process raw iEEG signals.
    • A repurposed iEEG dataset comprising 78 subjects was utilized.
    • Performance was evaluated through same-session and cross-session identification testing.

    Main Results:

    • Achieved a same-session identification accuracy of 95.84%.
    • Demonstrated resilience of iEEG signals against template aging in cross-session analysis.
    • Showcased incremental learning capabilities of the iEEG biometric system.

    Conclusions:

    • Intracranial electroencephalography (iEEG) signals are a feasible and effective modality for human biometric identification.
    • iEEG biometrics offer advantages over traditional EEG methods, particularly in overcoming template aging.
    • This proof-of-concept study opens new avenues for secure and reliable biometric solutions.