Doctor of Philosophy

dissertationRecording the neural activity of human subjects is indispensable for fundamental neuroscience research and clinical applications. Human studies range from examining the neural activity of large regions of the cortex using electroencephalography (EEG) or electrocorticography (ECoG) to single neurons or small populations of neurons using microelectrode arrays. In this dissertation, microscale recordings in the human cortex were analyzed during administration of propofol anesthesia and articulate movements such as speech, finger flexion, and arm reach. Recordings were performed on epilepsy patients who required long-term electrocorticographic monitoring and were implanted with penetrating or surface microelectrode arrays. We used penetrating microelectrode arrays to investigate the effects of propofol anesthesia on action potentials (APs) and local field potentials (LFPs). Increased propofol concentration correlated with decreased high-frequency power in LFP spectra and decreased AP firing rates, as well as the generation of large amplitude spike-like LFP activity; however, the temporal relationship between APs and LFPs remained relatively consistent at all levels of propofol anesthesia. The propofol-induced suppression of neocortical network activity allowed LFPs to be dominated by low-frequency spike-like activity, and correlated with sedation and unconsciousness. As the low-frequency spike-like activity increased, and the AP-LFP relationship became more predictable, firing rate encoding capacity was impaired. This suggests a mechanism for decreased information processing in the neocortex that accounts for propofol-induced unconsciousness. We also demonstrated that speech, finger, and arm movements can be decoded from LFPs recorded with dense grids of microelectrodes placed on the surface of human cerebral cortex for brain computer interface (BCI) applications using LFPs recorded over face-motor area, vocalized articulations of ten different words and silence were classified on a trial-by-trial basis with 82.4% accuracy. Using LFPs recorded over the hand area of motor cortex, three individual finger movements and rest were classified on a trial-by-trial basis with 62% accuracy. LFPs recorded over the arm area of motor cortex were used to continuously decode the arm trajectory with a maximum correlation coefficient of 0.82 in the x-direction and 0.76 in the y-direction. These findings demonstrate that LFPs recorded by micro-ECoG grids from the surface of the cerebral cortex contain sufficient information to provide rapid and intuitive control a BCI communication or motor prosthesis

Silicon Valley new focus on brain computer interface: hype or hope for new applications? [version 1; referees: 2 approved, 1 approved with reservations]

In the last year there has been increasing interest and investment into developing devices to interact with the central nervous system, in particular developing a robust brain-computer interface (BCI). In this article, we review the most recent research advances and the current host of engineering and neurological challenges that must be overcome for clinical application. In particular, space limitations, isolation of targeted structures, replacement of probes following failure, delivery of nanomaterials and processing and understanding recorded data. Neural engineering has developed greatly over the past half-century, which has allowed for the development of better neural recording techniques and clinical translation of neural interfaces. Implementation of general purpose BCIs face a number of constraints arising from engineering, computational, ethical and neuroscientific factors that still have to be addressed. Electronics have become orders of magnitude smaller and computationally faster than neurons, however there is much work to be done in decoding the neural circuits. New interest and funding from the non-medical community may be a welcome catalyst for focused research and development; playing an important role in future advancements in the neuroscience community

Silicon Valley new focus on brain computer interface: hype or hope for new applications?

In the last year there has been increasing interest and investment into developing devices to interact with the central nervous system, in particular developing a robust brain-computer interface (BCI). In this article, we review the most recent research advances and the current host of engineering and neurological challenges that must be overcome for clinical application. In particular, space limitations, isolation of targeted structures, replacement of probes following failure, delivery of nanomaterials and processing and understanding recorded data. Neural engineering has developed greatly over the past half-century, which has allowed for the development of better neural recording techniques and clinical translation of neural interfaces. Implementation of general purpose BCIs face a number of constraints arising from engineering, computational, ethical and neuroscientific factors that still have to be addressed. Electronics have become orders of magnitude smaller and computationally faster than neurons, however there is much work to be done in decoding the neural circuits. New interest and funding from the non-medical community may be a welcome catalyst for focused research and development; playing an important role in future advancements in the neuroscience community

Hybrid head cap for mouse brain studies

Abstract. In this thesis, I present a hybrid head cap in combination with non-invasive multi-channel Electroencephalogram (EEG) and Near-Infrared Spectroscopy (NIRS) to measure brainwaves on mice’s scalps. Laboratory animal research provides insights into multiple potential applications involving humans and other animals. An experimental framework that targets laboratory animals can lead to useful transnational research if it strongly reflects the actual application environment. The non-invasive head cap with three electrodes for EEG and two optodes for NIRS is suggested to measure brainwaves throughout the laboratory mice’s entire brain region without surgical procedures. The suggested hybrid head cap aims to ensure stability in vivo monitoring for mouse brain in a non-invasive way, similarly as the monitoring is performed for the human brain. The experimental part of the work to study the quality of the gathered EEG and fNIRS signals, and usability validation of the head cap, however, was not completed in the planned time frame of the thesis work

Nano-Watt Modular Integrated Circuits for Wireless Neural Interface.

In this work, a nano-watt modular neural interface circuit is proposed for ECoG neuroprosthetics. The main purposes of this work are threefold: (1) optimizing the power-performance of the neural interface circuits based on ECoG signal characteristics, (2) equipping a stimulation capability, and (3) providing a modular system solution to expand functionality. To achieve these aims, the proposed system introduces the following contributions/innovations: (1) power-noise optimization based on the ECoG signal driven analysis, (2) extreme low-power analog front-ends, (3) Manchester clock-edge modulation clock data recovery, (4) power-efficient data compression, (5) integrated stimulator with fully programmable waveform, (6) wireless signal transmission through skin, and (7) modular expandable design. Towards these challenges and contributions, three different ECoG neural interface systems, ENI-1, ENI-16, and ENI-32, have been designed, fabricated, and tested. The first ENI system(ENI-1) is a one-channel analog front-end and fabricated in a 0.25µm CMOS process with chopper stabilized pseudo open-loop preamplifier and area-efficient SAR ADC. The measured channel power, noise and area are 1.68µW at 2.5V power-supply, 1.69µVrms (NEF=2.43), and 0.0694mm^2, respectively. The fabricated IC is packaged with customized miniaturized package. In-vivo human EEG is successfully measured with the fabricated ENI-1-IC. To demonstrate a system expandability and wireless link, ENI-16 IC is fabricated in 0.25µm CMOS process and has sixteen channels with a push-pull preamplifier, asynchronous SAR ADC, and intra-skin communication(ISCOM) which is a new way of transmitting the signal through skin. The measured channel power, noise and area are 780nW, 4.26µVrms (NEF=5.2), and 2.88mm^2, respectively. With the fabricated ENI-16-IC, in-vivo epidural ECoG from monkey is successfully measured. As a closed-loop system, ENI-32 focuses on optimizing the power performance based on a bio-signal property and integrating stimulator. ENI-32 is fabricated in 0.18µm CMOS process and has thirty-two recording channels and four stimulation channels with a cyclic preamplifier, data compression, asymmetric wireless transceiver (Tx/Rx). The measured channel power, noise and area are 140nW (680nW including ISCOM), 3.26µVrms (NEF=1.6), and 5.76mm^2, respectively. The ENI-32 achieves an order of magnitude power reduction while maintaining the system performance. The proposed nano-watt ENI-32 can be the first practical wireless closed-loop solution with a practically miniaturized implantable device.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98064/1/schang_1.pd

Designing a Clinically Viable Brain Computer Interface for the Control of Neuroprosthetics

Currently no brain computer interfaces exist that can control the individual fingers of a hand prosthesis and is suitable for permanent implantation in and individual with a single limb amputation. Within this thesis a design for a novel minimally invasive brain computer interface system is proposed that would be relatively low risk, allow for control of a prosthesis using existing cortical structures and be suitable for patients with loss of a single limb. The early stage development and proof of concept work has been done taking into account relevant regulatory requirements, so that a finalised version of the design would be suitable for regulatory certification. This novel design is found to be worth pursuing and may in turn open up new research opportunities

Doctor of Philosophy

dissertationThis dissertation describes the use of cortical surface potentials, recorded with dense grids of microelectrodes, for brain-computer interfaces (BCIs). The work presented herein is an in-depth treatment of a broad and interdisciplinary topic, covering issues from electronics to electrodes, signals, and applications. Within the scope of this dissertation are several significant contributions. First, this work was the first to demonstrate that speech and arm movements could be decoded from surface local field potentials (LFPs) recorded in human subjects. Using surface LFPs recorded over face-motor cortex and Wernickes area, 150 trials comprising vocalized articulations of ten different words were classified on a trial-by-trial basis with 86% accuracy. Surface LFPs recorded over the hand and arm area of motor cortex were used to decode continuous hand movements, with correlation of 0.54 between the actual and predicted position over 70 seconds of movement. Second, this work is the first to make a detailed comparison of cortical field potentials recorded intracortically with microelectrodes and at the cortical surface with both micro- and macroelectrodes. Whereas coherence in macroelectrocorticography (ECoG) decayed to half its maximum at 5.1 mm separation in high frequencies, spatial constants of micro-ECoG signals were 530-700 ?m-much closer to the 110-160 ?m calculated for intracortical field potentials than to the macro-ECoG. These findings confirm that cortical surface potentials contain millimeter-scale dynamics. Moreover, these fine spatiotemporal features were important for the performance of speech and arm movement decoding. In addition to contributions in the areas of signals and applications, this dissertation includes a full characterization of the microelectrodes as well as collaborative work in which a custom, low-power microcontroller, with features optimized for biomedical implants, was taped out, fabricated in 65 nm CMOS technology, and tested. A new instruction was implemented in this microcontroller which reduced energy consumption when moving large amounts of data into memory by as much as 44%. This dissertation represents a comprehensive investigation of surface LFPs as an interfacing medium between man and machine. The nature of this work, in both the breadth of topics and depth of interdisciplinary effort, demonstrates an important and developing branch of engineering

A Closed-Loop Deep Brain Stimulation Device With a Logarithmic Pipeline ADC.

This dissertation is a summary of the research on integrated closed-loop deep brain stimulation for treatment of Parkinson’s disease. Parkinson's disease is a progressive disorder of the central nervous system affecting more than three million people in the United States. Deep Brain Stimulation (DBS) is one of the most effective treatments of Parkinson’s symptoms. DBS excites the Subthalamic Nucleus (STN) with a high frequency electrical signal. The proposed device is a single-chip closed-loop DBS (CDBS) system. Closed-loop feedback of sensed neural activity promises better control and optimization of stimulation parameters than with open-loop devices. Thanks to a novel architecture, the prototype system incorporates more functionality yet consumes less power and area compared to other systems. Eight front-end low-noise neural amplifiers (LNAs) are multiplexed to a single high-dynamic-range logarithmic, pipeline analog-to-digital converter (ADC). To save area and power consumption, a high dynamic-range log ADC is used, making analog automatic gain control unnecessary. The redundant 1.5b architecture relaxes the requirements for the comparator accuracy and comparator reference voltage accuracy. Instead of an analog filter, an on-chip digital filter separates the low frequency neural field potential signal from the neural spike energy. An on-chip controller generates stimulation patterns to control the 64 on-chip current-steering DACs. The 64 DACs are formed as a cascade of a single shared 2-bit coarse current DAC and 64 individual bi-directional 4-bit fine DACs. The coarse/fine configuration saves die area since the MSB devices tend to be large. Real-time neural activity was recorded with the prototype device connected to microprobes that were chronically implanted in two Long Evans rats. The recorded in-vivo signal clearly shows neural spikes of 10.2 dB signal-to-noise ratio (SNR) as well as a periodic artifact from neural stimulation. The recorded neural information has been analyzed with single unit sorting and principal component analysis (PCA). The PCA scattering plots from multi-layers of cortex represent diverse information from either single or multiple neural sources. The single-unit neural sorting analysis along with PCA verifies the feasibility of the implantable CDBS device for to in-vivo neural recording interface applications.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60733/1/milaca_1.pd

Advances in closed-loop deep brain stimulation devices

BACKGROUND: Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner. METHODS: This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research. RESULTS: Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state. CONCLUSIONS: The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized