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Updated: Jul 12, 2026
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.
Introduction:
Electroencephalogram (EEG)-based biometric recognition for brain-computer interfaces faces challenges from domain shifts, temporal nonstationarity, and limited scalability.
Methods:
To address these issues, we present DyAMNet, a framework that combines EEG microstate analysis with a hybrid attention mechanism. DyAMNet employs dynamic loss balancing to improve generalization and constructs a domain-invariant feature space that supports user expansion without catastrophic forgetting. We evaluated the model on three benchmark datasets (DEAP, THU-EP, and SEED).
Results:
The framework attains 87.2% accuracy in cross-dataset recognition and retains 84.0% accuracy when incrementally scaling to 60 users. The system also tolerates physiological artifacts and intersession signal drift, outperforming state-of-the-art models.
Discussion:
These findings show that dynamic adversarial training coupled with contrastive feature learning reduces brain-signal variability and preserves scalability. The work establishes a robust basis for feasible identity authentication and supports deploying brain-computer interfaces in clinical and everyday settings. The code is available at: https://github.com/cangtianhaoxue/DyAMNet.git.