CCE Theses and Dissertations
Date of Award
2015
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Information Systems (DCIS)
Department
College of Engineering and Computing
Advisor
Maxine S. Cohen
Committee Member
Sumitra Mukherjee
Committee Member
Bruce Montgomery
Keywords
Authentication, BCI, Biometrics, cybersecurity, HCI, Security, Computer engineering, Computer science, Information science
Abstract
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.
NSUWorks Citation
Ricardo J. Rodriguez. 2015. An Electroencephalogram (EEG) Based Biometrics Investigation for Authentication: A Human-Computer Interaction (HCI) Approach. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (67)
https://nsuworks.nova.edu/gscis_etd/67.