Advancing Brainwave-Based Biometrics: A Large-Scale, Multi-Session Evaluation

TL;DR

This large-scale study evaluates brainwave biometrics over multiple sessions, demonstrating deep learning's superiority, the importance of re-enrollment, sensor reduction feasibility, and providing open-source tools for future research.

Contribution

The paper presents a comprehensive multi-year, multi-session dataset and analysis, highlighting deep learning advantages and sensor reduction, advancing brainwave biometric research.

Findings

Deep learning outperforms hand-crafted features.

EER increases over time, from 6.7% after 1 day to 14.3% after a year.

Fewer sensors can be used with acceptable accuracy loss.

Abstract

The field of brainwave-based biometrics has gained attention for its potential to revolutionize user authentication through hands-free interaction, resistance to shoulder surfing, continuous authentication, and revocability. However, current research often relies on single-session or limited-session datasets with fewer than 55 subjects, raising concerns about the generalizability of the findings. To address this gap, we conducted a large-scale study using a public brainwave dataset comprising 345 subjects and over 6,007 sessions (an average of 17 per subject) recorded over five years using three headsets. Our results reveal that deep learning approaches significantly outperform hand-crafted feature extraction methods. We also observe Equal Error Rates (EER) increases over time (e.g., from 6.7% after 1 day to 14.3% after a year). Therefore, it is necessary to reinforce the enrollment set after successful login attempts. Moreover, we demonstrate that fewer brainwave measurement sensors can be used, with an acceptable increase in EER, which is necessary for transitioning from medical-grade to affordable consumer-grade devices. Finally, we compared our results to prior work and existing biometric standards. While our performance is on par with or exceeds previous approaches, it still falls short of industrial benchmarks. Based on the results, we hypothesize that further improvements are possible with larger training sets. To support future research, we have open-sourced our analysis code.