(EEG) Based Biometrics Investigation for Authentication

An Encephalogram (EEG) Based Biometrics Investigation for Authentication: A HumanComputer Interaction (HCI) Approach
Ricardo J. Rodriguez – Advisor: Dr. Maxine Cohen
Problem Statement
Traditional authentication methods rely on
“what you know” (i.e., knowledge-based
authenticator) or “what you have” (i.e., objectbased authenticator) to identify users. These
are susceptible to inadvertent disclosure or
can be simply lost or stolen (O’Gorman,
2003).“What you are” (i.e., ID-based
authenticator) clearly provides an edge since
individuals are “who they are” regardless of
“what they know” or “what they have”.
Additionally, EEG devices are undergoing a
significant evolution that is leading to their
acceptance as Brain-Computer Interfaces
(BCI) by an increasing number of users
(Minnery and Fine, 2009). This creates an
opportunity to exploit the unique “inner-self” of
a person for authentication purposes.
EEG Based Biometrics System
Visual Stimuli
Auditory Stimuli
Tactile Stimuli
Olfactory Stimuli
Gustatory Stimuli
Solution Approach
• Leverage emotionally meaningful visual stimuli
• Record resulting Visual Evoked Potentials
(VEPs) as EEG signals
• Process and extract features from EEG Signals
• Develop AI Model (e.g., Support Vector Machine)
• Analyze results to determine accuracy
• Measure User Acceptance
Key Resources
Research Goal
Research Questions
The goal of this work is to develop and
evaluate the effectiveness of an EEG-based
biometric authentication mechanism to
addresses some of the common shortfalls of
current systems. The proposed approach
leverages the unique “inner-self” of a person,
which is expected to be different between
people performing similar tasks (Zúquete,
Quintela, and Cunha, 2011).
RQ1. What level of accuracy will the
proposed EEG-based biometric access
control mechanism provide? – Key Metrics:
False Acceptance Rate (FAR), False
Rejection Rate (FRR), and Equal Error Rate
RQ2. How will the proposed EEG-based
biometric access control mechanism be
perceived by users (i.e., acceptability and
user satisfaction)?
Emotiv EEG Headset
Emotiv Research SDK
HCILab and BCILab
Key References
Minnery, B. S., & Fine, M. S. (2009). Feature: Neuroscience
and the future of human-computer interaction. Interactions,
16 (2), 70-75. doi:10.1145/1487632.1487649
O'Gorman, L. (2003). Comparing passwords, tokens, and
biometrics for user authentication. Proceedings of the IEEE,
91(12), 2021-2040. doi:10.1109/jproc.2003.819611
Zúquete, A., Quintela, B., & Cunha, J. (2011). Biometric
Authentication with Electroencephalograms: Evaluation of Its
Suitability Using Visual Evoked Potentials. In A. Fred, J.
Filipe & H. Gamboa (Eds.), Biomedical Engineering Systems
and Technologies (Vol. 127, pp. 290-306): Springer Berlin