BioE PhD Thesis Defense 

Musa Syed Mahmood 

November 18th, 2021, 11:00 AM 

Location: Pettit 102; Link: https://bluejeans.com/317389848/5418  

Advisor: 

Woon-Hong Yeo, Ph.D. ME, Georgia Institute of Technology 

Committee Members:  

Frank L. Hammond III, Ph.D.  

ME, Georgia Institute of Technology 

Peter J. Hesketh, Ph.D.  

ME, Georgia Institute of Technology  

Seungwoo Lee, Ph.D.  

ME, Georgia Institute of Technology  

Audrey Duarte, Ph.D.  

Psychology, University of Texas at Austin 

Study of soft materials, flexible electronics, and machine learning for fully portable and wireless brain-machine interfaces 

Advancing technology has increasingly allowed for individuals suffering from severe disabilities (e.g. locked-in syndrome) to allow for movement or communication. These technologies include low-power wireless protocols, wearable, battery powered devices, flexible electronics, biocompatible skin-interfaced materials, and advanced machine learning techniques. Current non-invasive brain-machine interfaces are limited in scope, have limited functionality, and primarily only used in research environments. The more recent move towards wearable, wireless system has enabled greater mobility. However, issues remain – these systems are bulky, uncomfortable to wear, and often involve the use of messy conductive pastes and gels. In this dissertation, we introduce a flexible electronics platform, SKINTRONICS, with a comprehensive redesign of an EEG-based brain-machine interface with novel implementation of flexible EEG, flexible and stretchable interconnects, replica-molded microneedle electrodes, and advanced machine learning techniques for improved signal to noise ratio and reduced noise and motion artefacts. A set of general improvements and novel advancements are examined and applied in real-world electroencephalography-based brain-machine interfaces. These real-world improvements and applications show potential for restoring function to and improving quality of life of severely disabled subjects. Wireless, wearable electroencephalograms and dry non-invasive electrodes can be utilized to allow recording of brain activity on a mobile subject to allow for unrestricted movement. Additionally, multilayer microfabricated flexible circuits, combined with a soft materials platform, provide imperceptible wearable electronics for wireless, portable, long-term recording of brain signals. This dissertation focuses on sharing the study outcomes in soft materials, flexible electronics, and machine learning for universal brain-machine interfaces that could offer remedies in communication and movement for these individuals.