Ph.D. Thesis Defense - Gina M. Notaro

Ph.D. Thesis Defense - Gina M. Notaro

Dartmouth Events

Ph.D. Thesis Defense - Gina M. Notaro

“Development and Integration of Low-Cost Bio-Measurement Systems for Neuroscience Research”

Tuesday, April 25, 2017
Jackson Conf Room, Cummings Hall
Intended Audience(s): Public

Thesis Committee

Solomon Diamond, Ph.D.  (Advisor)

Kofi Odame, Ph.D.

Jeremy Manning, Ph.D.

Per Sederberg, Ph.D.




Over the past several years, the emergence of consumer electroencephalography (EEG) devices, inexpensive biosensors, open-source software, and improved computing capabilities has allowed for new opportunities in neuroscientific research. Among these possible applications include mobile and scalable data collection, as well as acquisition of neurophysiological signals within nontraditional contexts. However, as consumer devices often do not incorporate features for optimal research use, systems development efforts are required prior to research implementation. Such development includes signal quality and timing evaluation of the low-cost hardware within consumer devices, as well additional improvements to the device hardware and software. Thus, the presented thesis first examines the usability of such consumer EEG and eye-tracking devices with respect to signals of interest to the neuroscientific community, by establishing methods for signal quality evaluation broadly applicable to neuroscientific research. Following this assessment, the selected OpenBCI EEG board and Gazepoint GP3 eye-tracker were integrated along with an open-source macro (AutoHotKey) script to monitor screen content and user activity. This combined platform provides a framework for investigation of neurophysiological signals within nontraditional research paradigms (i.e. paradigms not requiring the use of experimental paradigm software). The capabilities of the integrated system within an unconstrained environment were demonstrated using Duolingo, a free language learning web site. The final part of this work describes initial testing of an inexpensive, portable, and scalable standalone EEG data acquisition system, combining the OpenBCI EEG board with a Raspberry Pi computer (OpenBC-Pi system). Overall, this work presents systems development and integration to provide the initial framework for investigation of complex, real-world neurophysiological phenomena.

For more information, contact:
Daryl Laware

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