[CCoE Notice] Cullen College Dissertation Announcement

[Hutchinson, Inez A]

[Dissertation Defense Announcement at the Cullen College of Engineering]
Brain-eNet: Towards an Enabling Technology for BCI-IoT Systems
Juan Jose Gonzalez-Espana

April 22, 2024; 2:00 PM - 4:00 PM (CDT)
Location: E413 Eng Bld 2
Zoom: https://urldefense.com/v3/__https://uh-edu-cougarnet.zoom.us/j/88373492846?pwd=R2xrS3k0d0RCc2JhK3NPOEpSajVKdz09__;!!LkSTlj0I!FhFMWA2WkReqpbGzQiJfdssTemv6U0IJSs8uZCqNIRW_p60N6MX3C_oXiVl325vm6b_MPSb3VtTJfC4qnMdtLwmBO9g$ 

Committee Chair:

Jose L. Contreras-Vidal, Ph.D.

                                                           Committee Members:                                                                          Saurabh Prasad, Ph.D. | Hung Le, Ph.D. | Elham Morshedzadeh, Ph.D. | Xin Fu, Ph.D

Abstract

Over the past decade, there has been a remarkable exponential growth in both research and industrial applications of the Internet of Things (IoT). An emerging trend within this field involves the integration with Brain-Computer Interface (BCI) systems, an additional communication channel of the brain with the world, called BCIoT. This convergence of technologies holds the potential for significant impacts on both Brain-Machine (such as Brain-Robot , Brain-Phone , Brain-Assisting Equipment ) and Brain to Brain interactions (such as more immersive classrooms, meetings, patient care). Though, it is crucial to highlight that there is currently a lack of standards, preferred architectures, and enabling platforms that could foster or drive the growth of this field. Furthermore, the practical deployment of such implementations is predominantly restricted to clinical environments, as the majority of BCI systems are designed for laboratory settings and necessitate the expertise of a skilled technician.

In this dissertation, it is proposed a novel BCIoT platform, namely Brain-eNet, to address the above challenges and limitations of current systems. Brain-eNet has been developed considering user-centered design principles and the domain-specific constraints-space defined by BCIoT real-time mobile applications. The first BCIoT implementation of the proposed platform is a neurorehabilitation application. The platform underwent a longitudinal study involving five stroke survivors, and five healthy subjects. Through the invaluable insights gained from human subject interaction, several areas for system enhancement were identified. These findings, combined with a comprehensive literature review, facilitated the formulation of a methodology and architecture specifically tailored for BCIoT applications.

The proposed methodology and architecture hold significant potential for expediting the advancement of modular BCIoT systems characterized by interoperability, reliability, performance, and usability.


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