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

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Shiwei, Cheng, Jialing, Wang, Danyi, Sheng, Yijian, Chen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Authentication; Biometrics; Biometrics (access control); brain computer interaction; electroencephalogram; Electroencephalography; Eye movements; eye tracking; Feasibility studies; Feature extraction; Gaze tracking; Human-computer interface; privacy; Security; Surfing; Task analysis
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3241081

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 few researches 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 proved to be useful in reducing the possibility of user misidentification. Our approach hold great potential in providing references for implementing hybrid BCI authentication for anti-shoulder surfing applications.