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Related Experiment Video

Updated: Jul 12, 2026

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

DyAMNet: dynamic adversarial and contrastive network for EEG biometrics.

Ting Li1,2, MengFan Li1, Ran Sun1

  • 1School of Computational Science and Computer Science, Xi'an Polytechnic University, Xi'an, China.

Frontiers in Neuroscience
|July 11, 2026
PubMed
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DyAMNet improves electroencephalogram (EEG) biometric recognition for brain-computer interfaces by reducing brain-signal variability. This framework enhances scalability and robustness for real-world applications.

Area of Science:

  • Neuroscience
  • Biometrics
  • Machine Learning

Background:

  • Electroencephalogram (EEG)-based biometric recognition for brain-computer interfaces (BCIs) faces significant challenges.
  • These challenges include domain shifts, temporal nonstationarity, and limited scalability, hindering practical BCI deployment.

Purpose of the Study:

  • To introduce DyAMNet, a novel framework designed to overcome the limitations of EEG-based biometric recognition.
  • The framework aims to improve generalization, enable user expansion without catastrophic forgetting, and create a domain-invariant feature space.

Main Methods:

  • DyAMNet integrates EEG microstate analysis with a hybrid attention mechanism.
  • It employs dynamic loss balancing for improved generalization and contrastive feature learning to reduce brain-signal variability.
Keywords:
biometric recognitioncontrastive learningdynamic adversarial learningelectroencephalogram (EEG)identity authentication

Related Experiment Videos

Last Updated: Jul 12, 2026

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

  • The framework was evaluated on three benchmark datasets: DEAP, THU-EP, and SEED.

    • Main Results:

    • DyAMNet achieved 87.2% accuracy in cross-dataset recognition.
    • It maintained 84.0% accuracy when incrementally scaling to 60 users.
    • The system demonstrated tolerance to physiological artifacts and intersession signal drift, outperforming existing state-of-the-art models.

    Conclusions:

    • Dynamic adversarial training and contrastive feature learning effectively reduce brain-signal variability and enhance scalability.
    • The DyAMNet framework provides a robust foundation for reliable identity authentication.
    • This work supports the deployment of BCIs in both clinical and everyday settings.