NeuroPriv: Adversarial Representation Learning for Privacy in Wearable EEG Systems
Wearable EEG systems may expose sensitive information beyond their intended health function, creating substantial risks to neuroprivacy. In this work, we show that commonly used EEG features can reveal participant identity and demographic attributes in addition to supporting the intended cognitive task. Wearable EEG is increasingly being explored for cognitive monitoring, neurological assessment, and longitudinal digital-health applications, yet many systems assume that transmitting compact spectral or spatial features instead of raw EEG provides sufficient privacy protection. Using EEGMAT as a motivating case study, we find that compact EEG features achieve a balanced accuracy of 0.788 f...
| Authors | Sarmistha Sarna Gomasta, Bhawana Chhaglani, Prashant Shenoy |
|---|---|
| Primary category | cs.CR |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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