文献类型：期刊文献

英文题名：EEGAuth: A Secure and Lightweight EEG-Based System Integrating Authentication and Key Generation

作者：Han, Xun[1,2];Xiao, Jun[3,4];Liu, Yifan[3,4];Zhang, Ruilin[3,4];Zhu, Biaokai[5];Hao, Hongyi[6];Li, Youqi[7];Li, Fan[7];Zhang, Qian[3,4]

机构：[1]Sichuan Police Coll, Dept Transportat Management, Luzhou 646000, Peoples R China;[2]Intelligent Policing Key Lab Sichuan Prov, Luzhou 646000, Peoples R China;[3]Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200231, Peoples R China;[5]Shanxi Police Coll, Taiyuan 030401, Peoples R China;[6]Shanxi Inst Technol, Yangquan 045000, Peoples R China;[7]Beijing Inst Technol, Beijing 100811, Peoples R China

年份：2025

卷号：12

期号：24

起止页码：55330

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;EI(收录号：20254419409721);WOS:【SCI-EXPANDED(收录号:WOS:001648269400005)】；

基金：The work of Xun Han and Qian Zhang was supported in part by the Intelligent Policing Key Laboratory of Sichuan Province under Grant ZNJW2024KFQN006. The work of Biaokai Zhu was supported in part by the National Natural Science Foundation of China under Grant 62306207, in part by the Intelligent Policing Key Laboratory of Sichuan Province under Grant ZNJW2022KFZD004, in part by the Basic Research Plan of Shanxi Province under Grant 202303021211339, in part by the Program for the Young Academic Leaders of Higher Learning Institutions of Shanxi under Grant 2024Q042, and in part by Shanxi Provincial Higher Education Teaching Reform and Innovation Project under Grant J20241576. (Xun Han and Jun Xiao are co-first authors.)

语种：英文

外文关键词：Electroencephalography; Authentication; Security; Feature extraction; Accuracy; Biometrics; Brain modeling; Immune system; Discrete wavelet transforms; Indexes; biometric security; electroencephalography (EEG); key generation

摘要：Electroencephalography (EEG) signals have emerged as a novel biometric feature in identity authentication. However, in highly sensitive scenarios such as remote access control and sensitive operation confirmation, identity authentication alone is insufficient to ensure system security. This article proposes EEGAuth, an EEG-based secure and lightweight authentication system with cryptographic key generation, addressing the demand for integrated systems that enhance both security and user convenience by combining identity authentication and key generation into a unified solution. The proposed system employs a genetic algorithm (GA) for optimal channel selection, integrates a discrete wavelet transform (DWT) with an autoencoder-based feature extraction framework, and implements a convolutional neural network (CNN)-based architecture for robust identity authentication. In addition, the system discretizes feature vectors to generate unique and repeatable seeds, which are used as inputs to a secure hash function to produce keys. The evaluation results show that our model achieves a classification accuracy of 99.38% with only 15 channels, significantly outperforming state-of-the-art methods and baseline models. The generated cryptographic keys demonstrate robust security properties, as evidenced by their successful passage through the NIST statistical test suite for randomness verification, scale index analysis for aperiodicity assessment, and autocorrelation testing for bit-sequence independence, collectively confirming their resistance to cryptographic attacks and compliance with security standards.