Neural-LWE: a biometric-anchored authenticated key agreement for post-quantum brain–computer interfaces
The proliferation of Brain-Computer Interfaces (BCIs) and wireless Electroencephalography (EEG) devices necessitates security protocols that are simultaneously quantum-resistant and intrinsically linked to users’ biological identity. While standard post-quantum cryptography like ML-KEM provides foundational security, it fails to address critical neurotechnology requirements: integrated biometric authentication, efficient long-term session management, and resilience to signal noise. This paper presents Neural-LWE (NLWE), a novel biometric-anchored authenticated key agreement protocol for quantum-secure Brain-Computer Interfaces. We introduce five fundamental innovations: (1) integration of Kalman-filtered EEG features with ML-KEM encapsulation for biometric two-factor authentication; (2) Zero-Communication-Round Rekeying (ZCRR) enabling 250$$\times$$ more efficient forward secrecy updates; (3) entropy-adaptive security dynamically scaling lattice parameters based on real-time EEG quality; (4) ternary-entropy masking for side-channel resistance; and (5) manifold-based anomaly detection against presentation attacks. NLWE establishes IND-CCA2 security under the Module-LWE (MLWE) assumption while addressing unique BCI constraints through formal cryptographic-biometric binding. Implementation results using physiologically plausible synthetic data confirm that ZCRR achieves 0.45$$\mu$$J rekeying energy with sub-millisecond latency. While experimental validation is performed in a controlled environment, the results indicate NLWE is a promising proof-of-concept for continuous neural applications requiring persistent quantum security.
Authors
- Hassan Nasiraee (ORCID: https://orcid.org/0000-0002-5638-7772)
- Farid Samsami-Khodadad
- Ximeng Liu (ORCID: https://orcid.org/0000-0002-4238-3295)
- Fakhroddin Nazari (ORCID: https://orcid.org/0000-0001-9905-8090)
Institutions
- Amol University of Special Modern Technologies (IR)
- Peng Cheng Laboratory (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- Scientific Reports
- Published
- 2026-04-21
- DOI
- https://doi.org/10.1038/s41598-026-48527-x
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00