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Proceedings Paper

Authentication, privacy, security can exploit brainwave by biomarker
Author(s): Jeffrey Jenkins; Charles Sweet; James Sweet; Steven Noel; Harold Szu
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Paper Abstract

We seek to augment the current Common Access Control (CAC) card and Personal Identification Number (PIN) verification systems with an additional layer of classified access biometrics. Among proven devices such as fingerprint readers and cameras that can sense the human eye's iris pattern, we introduced a number of users to a sequence of 'grandmother images', or emotionally evoked stimuli response images from other users, as well as one of their own, for the purpose of authentication. We performed testing and evaluation of the Authenticity Privacy and Security (APS) brainwave biometrics, similar to the internal organ of the human eye’s iris which cannot easily be altered. ‘Aha’ recognition through stimulus-response habituation can serve as a biomarker, similar to keystroke dynamics analysis for inter and intra key fluctuation time of a memorized PIN number (FIST). Using a non-tethered Electroencephalogram (EEG) wireless smartphone/pc monitor interface, we explore the appropriate stimuli-response biomarker present in DTAB low frequency group waves. Prior to login, the user is shown a series of images on a computer display. They have been primed to click their mouse when the image is presented. DTAB waves are collected with a wireless EEG and are sent via Smartphone to a cloud based processing infrastructure. There, we measure fluctuations in DTAB waves from a wireless, non-tethered, single node EEG device between the Personal Graphic Image Number (PGIN) stimulus image and the response time from an individual’s mental performance baseline. Towards that goal, we describe an infrastructure that supports distributed verification for web-based EEG authentication. The performance of machine learning on the relative Power Spectral Density EEG data may uncover features required for subsequent access to web or media content. Our approach provides a scalable framework wrapped into a robust Neuro-Informatics toolkit, viable for use in the Biomedical and mental health communities, as well as numerous consumer applications.

Paper Details

Date Published: 19 June 2014
PDF: 13 pages
Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 91180U (19 June 2014); doi: 10.1117/12.2051323
Show Author Affiliations
Jeffrey Jenkins, George Mason Univ. (United States)
Charles Sweet, George Mason Univ. (United States)
Loft Mind, Inc. (United States)
James Sweet, Loft Mind, Inc. (Germany)
Steven Noel, George Mason Univ. (United States)
Harold Szu, The Catholic Univ. of America (United States)

Published in SPIE Proceedings Vol. 9118:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
Harold H. Szu; Liyi Dai, Editor(s)

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