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arXiv cs.LG
arXiv cs.LG
6/23/2026
NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

Short summary

NeuroShield, a new foundation model, solves a key EEG authentication challenge: existing models fail when recording hardware, channel layouts, or signal duration change. Pretrained on 15,762 subjects across three datasets, it reduces error rates by 0.44–8.06 percentage points and generalizes to unseen hardware configurations. The researchers released it open-source to enable reproducible, reusable EEG authentication across diverse settings.

  • •Addresses hardware/configuration fragmentation in EEG authentication through a reusable foundation model architecture
  • •Achieves 0.44–8.06 percentage point error rate reduction over state-of-the-art after fine-tuning on new datasets
  • •Generalizes to longer segments and unseen headset channel layouts; released open-source for reproducibility

Generated with AI, which can make mistakes.

#research-breakthrough#open-source
Read full article at arXiv cs.LG

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