Identification With Your Mind: A Hybrid BCI-Based Authentication Approach for Anti-Shoulder-Surfing Attacks Using EEG and Eye Movement Data

被引：11

作者：

Cheng, Shiwei ^{[1
]}

Wang, Jialing ^{[1
]}

Sheng, Danyi ^{[1
]}

Chen, Yijian ^{[1
]}

机构：

[1] Zhejiang Univ Technol, Sch Comp Sci, Hangzhou 310023, Peoples R China

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2023年 / 72卷

基金：

中国国家自然科学基金;

关键词：

Biometrics; brain-computer interface (BCI); electroencephalogram (EEG); eye tracking; privacy; security; BIOMETRICS;

D O I：

10.1109/TIM.2023.3241081

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Biometric authentication has been applied in many domains due to the promoting awareness of privacy and security risks. Most of the previous work has shown the performance of single biometric, but a few studies explored the feasibility of hybrid biometrics. On this basis, we proposed a hybrid brain-computer interface (BCI) authentication approach that combined user's electroencephalogram (EEG) and eye movement data features simultaneously. In anti-shoulder-surfing experiments, the proposed approach reached the average accuracy of 84.36% (the highest was 88.35%) to identify shoulder surfers and outperformed the only EEG and only eye movement data-based authentication approach. In additional experiments, the approach was proven to be useful in reducing the possibility of user misidentification. Our approach holds a great potential in providing references for implementing hybrid BCI authentication for anti-shoulder-surfing applications.

引用

页数：14