arXiv Machine Learning
NeuroPriv: Adversarial Representation Learning for Privacy in Wearable EEG Systems
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arXiv Machine Learning
Sep 11
Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology
The paper investigates how subject‑level differential privacy (DP) can anonymize EEG‑derived feature representations while preserving clinical utility. It evaluates Gaussian and Laplace perturbations across three deployment scenarios—client‑side, server‑side, and decentralized local training—using statistical utility metrics and a downstream machine‑learning task. Results indicate that DP can be integrated into EEG workflows, but the choice of mechanism, privacy parameters, and sensitivity calibration critically affects data utility, especially in small, imbalanced clinical datasets.
arXiv Machine Learning
Jun 25
NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication
arXiv:2606. 20673v2 Announce Type: replace Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained.
arXiv Machine Learning
Aug 19
SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models
The paper introduces SW-ProxyCE, a zero-query adversarial attack that exploits publicly released EEG foundation encoders to generate transferable adversarial examples for private downstream models. By using a small labeled reference set and shrinkage-whitened class prototypes, the method recovers task-level decision geometry without training a surrogate classifier. Experiments across three EEG tasks and multiple encoders show that SW-ProxyCE consistently outperforms task-agnostic attacks, demonstrating that the strong transferability of EEG foundation models does not guarantee adversarial robustness.
arXiv Machine Learning
Jul 30
SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval
arXiv:2603. 17109v2 Announce Type: replace Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction.
arXiv Machine Learning
Jun 3
Making Brain-Computer Interfaces More Secure
arXiv:2606. 02597v1 Announce Type: new Abstract: The development of brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) has advanced significantly mainly to machine learning.
arXiv Machine Learning
Aug 11
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
arXiv:2506. 19141v3 Announce Type: replace-cross Abstract: Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task.