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Dissertation/Thesis Abstract

An Electroencephalogram (EEG) Based Biometrics Investigation for Authentication: A Human-Computer Interaction (HCI) Approach
by Rodriguez, Ricardo J., Ph.D., Nova Southeastern University, 2015, 227; 3723216
Abstract (Summary)

Encephalogram (EEG) devices are one of the active research areas in human-computer interaction (HCI). They provide a unique brain-machine interface (BMI) for interacting with a growing number of applications. EEG devices interface with computational systems, including traditional desktop computers and more recently mobile devices. These computational systems can be targeted by malicious users. There is clearly an opportunity to leverage EEG capabilities for increasing the efficiency of access control mechanisms, which are the first line of defense in any computational system.

Access control mechanisms rely on a number of authenticators, including “what you know”, “what you have”, and “what you are”. The “what you are” authenticator, formally known as a biometrics authenticator, is increasingly gaining acceptance. It uses an individual’s unique features such as fingerprints and facial images to properly authenticate users. An emerging approach in physiological biometrics is cognitive biometrics, which measures brain’s response to stimuli. These stimuli can be measured by a number of devices, including EEG systems.

This work shows an approach to authenticate users interacting with their computational devices through the use of EEG devices. The results demonstrate the feasibility of using a unique hard-to-forge trait as an absolute biometrics authenticator by exploiting the signals generated by different areas of the brain when exposed to visual stimuli. The outcome of this research highlights the importance of the prefrontal cortex and temporal lobes to capture unique responses to images that trigger emotional responses.

Additionally, the utilization of logarithmic band power processing combined with LDA as the machine learning algorithm provides higher accuracy when compared against common spatial patterns or windowed means processing in combination with GMM and SVM machine learning algorithms. These results continue to validate the value of logarithmic band power processing and LDA when applied to oscillatory processes.

Indexing (document details)
Advisor: Cohen, Maxine S.
Commitee: Montgomery, Bruce, Mukherjee, Sumitra
School: Nova Southeastern University
Department: Computer Information Systems
School Location: United States -- Florida
Source: DAI-B 77/01(E), Dissertation Abstracts International
Subjects: Computer Engineering, Information science, Computer science
Keywords: Authentication, Bci, Cybersecurity, Hci, Security
Publication Number: 3723216
ISBN: 978-1-339-05916-7
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