Enhancing Graphical Authentication with EEG: A Study on Neural Responses and AI-Driven Security

The development of reliable authentication schemes is a significant challenge due to vulnerabilities like shoulder-surfing and impersonation attacks. This paper presents a six-month in-lab study (N=30) during which we designed and evaluated a novel two-factor authentication prototype combining Microsoft's Picture Gesture Authentication (PGA) with Electroencephalography (EEG). We aimed to leverage users' unique neural responses during pictorial processing in PGA, in order to train individual AI models that could mitigate impersonation attacks. Additionally, our study examined how users' familiarity with background images affected the accuracy of these models. Results indicated that: a) familiar stimuli enhance cognitive efficiency and authentication accuracy; b) the combination of Wavelet features, Hjorth parameters, and spectral entropy with Support Vector Machines yielded good performance. These findings suggest that EEG-based technology has the potential to improve graphical authentication systems, offering more secure alternatives to traditional methods.