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Updated: Jan 9, 2026

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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
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    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.

    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.

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    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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    Related Experiment Videos

    Last Updated: Jan 9, 2026

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

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    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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    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.