NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication
A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained. In particular, variations in headset hardware, channel layout, and signal duration create heterogeneous recordings that existing models are not designed to handle, causing each new headset or dataset to be treated as a separate model-development problem. This fragmentation limits multi-dataset learning, hinders knowledge transfer, and reduces model reusability. To address this limitation, we present NeuroShield, a reusable foundation model for EEG authentication that learns identity-discriminative embeddings from variable-channel and variable-length EEG record...
| Authors | Matin Fallahi, Patricia Arias-Cabarcos, Thorsten Strufe |
|---|---|
| Primary category | cs.LG |
| Published | 12 June 2026 |
| Last updated at source | 24 June 2026 |
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