Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Department

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

Available for download on Monday, December 11, 2017

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