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.

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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
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article

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

Hassan Nasiraee, Farid Samsami-Khodadad, Ximeng Liu, Fakhroddin Nazari
Scientific Reports
EEG and Brain-Computer Interfaces
article

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

Hassan Nasiraee, Farid Samsami-Khodadad, Ximeng Liu, Fakhroddin Nazari
article en

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$$\times$$ 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.

Scientific Reports
Amol University of Special Modern Technologies (IR), Peng Cheng Laboratory (CN), Fuzhou University (CN)
Openalex Percentile: Top 6%
EEG and Brain-Computer Interfaces
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