Sci Rep. 2026 Apr 21;16(1):18505.doi: 10.1038/s41598-026-48527-x.(IF:4.9).

本文采用的英格恩产品: CCAA细胞凋亡检测试剂盒(PI)

Neural-LWE: a biometric-anchored authenticated key agreement for post-quantum brain-computer interfaces

Affiliations

  • 1 Faculty of Engineering Modern Technologies, Amol University of Special Modern Technologies, Amol, Iran. Nasiraee@ausmt.ac.ir.
  • 2 Faculty of Engineering Modern Technologies, Amol University of Special Modern Technologies, Amol, Iran.
  • 3 College of Mathematics and Computer Science, Fuzhou University, Fujian, China.
  • 4 Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China.

Abstract

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× 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 0.45μJ 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.

Keywords: Biometric cryptography; Brain–Computer Interface; Key agreement; Learning with errors; Wireless EEG.

https://doi.org/10.1038/s41598-026-48527-x

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