Document Type
Dissertation
Degree
Doctor of Philosophy (PhD)
Major/Program
Electrical Engineering
First Advisor's Name
Hai Deng
First Advisor's Committee Title
Committee chair
Second Advisor's Name
Sakhrat Khizroev
Second Advisor's Committee Title
Committee member
Third Advisor's Name
Wei-Chiang Lin
Third Advisor's Committee Title
Committee member
Fourth Advisor's Name
Armando Barreto
Fourth Advisor's Committee Title
Committee member
Fifth Advisor's Name
Nezih Pala
Fifth Advisor's Committee Title
Committee member
Sixth Advisor's Name
Ismail Guvenc
Sixth Advisor's Committee Title
Committee member
Seventh Advisor's Name
Deng Pan
Seventh Advisor's Committee Title
Committee member
Keywords
Brain-Machine-Brain Interface (BMBI), Deep Brain Stimulation, RFID
Date of Defense
11-10-2016
Abstract
Machine collaboration with the biological body/brain by sending electrical information back and forth is one of the leading research areas in neuro-engineering during the twenty-first century. Hence, Brain-Machine-Brain Interface (BMBI) is a powerful tool for achieving such machine-brain/body collaboration. BMBI generally is a smart device (usually invasive) that can record, store, and analyze neural activities, and generate corresponding responses in the form of electrical pulses to stimulate specific brain regions. The Smart Brain-Machine-Brain-Interface (SBMBI) is a step forward with compared to the traditional BMBI by including smart functions, such as in-electrode local computing capabilities, and availability of cloud connectivity in the system to take the advantage of powerful cloud computation in decision making.
In this dissertation work, we designed and developed an innovative form of Smart Brain-Machine-Brain Interface (SBMBI) and studied its feasibility in different biomedical applications. With respect to power management, the SBMBI is a semi-passive platform. The communication module is fully passive—powered by RF harvested energy; whereas, the signal processing core is battery-assisted. The efficiency of the implemented RF energy harvester was measured to be 0.005%.
One of potential applications of SBMBI is to configure a Smart Deep-Brain-Stimulator (SDBS) based on the general SBMBI platform. The SDBS consists of brain-implantable smart electrodes and a wireless-connected external controller. The SDBS electrodes operate as completely autonomous electronic implants that are capable of sensing and recording neural activities in real time, performing local processing, and generating arbitrary waveforms for neuro-stimulation. A bidirectional, secure, fully-passive wireless communication backbone was designed and integrated into this smart electrode to maintain contact between the smart electrodes and the controller. The standard EPC-Global protocol has been modified and adopted as the communication protocol in this design. The proposed SDBS, by using a SBMBI platform, was demonstrated and tested through a hardware prototype. Additionally the SBMBI was employed to develop a low-power wireless ECG data acquisition device. This device captures cardiac pulses through a non-invasive magnetic resonance electrode, processes the signal and sends it to the backend computer through the SBMBI interface. Analysis was performed to verify the integrity of received ECG data.
Identifier
FIDC001261
Recommended Citation
Khan, Muhammad S., "Design and Development of Smart Brain-Machine-Brain Interface (SBMIBI) for Deep Brain Stimulation and Other Biomedical Applications" (2016). FIU Electronic Theses and Dissertations. 2724.
https://digitalcommons.fiu.edu/etd/2724
Included in
Biomedical Commons, Systems and Communications Commons, VLSI and Circuits, Embedded and Hardware Systems Commons
Rights Statement
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).