A direct-sequence spread-spectrum communication system for integrated sensor microsystems

Some of the most important challenges in health-care technologies have been identified to be development of noninvasive systems and miniaturization. In developing the core technologies, progress is required in pushing the limits of miniaturization, minimizing the costs and power consumption of microsystems components, developing mobile/wireless communication infrastructures and computing technologies that are reliable. The implementation of such miniaturized systems has become feasible by the advent of system-on-chip technology, which enables us to integrate most of the components of a system on to a single chip. One of the most important tasks in such a system is to convey information reliably on a multiple-access-based environment. When considering the design of telecommunication system for such a network, the receiver is the key performance critical block. The paper describes the application environment, the choice of the communication protocol, the implementation of the transmitter and receiver circuitry, and research work carried out on studying the impact of input data characteristics and internal data path complexity on area and power performance of the receiver. We provide results using a test data recorded from a pH sensor. The results demonstrate satisfying functionality, area, and power constraints even when a degree of programmability is incorporated in the system

Electronic bidirectional interfaces to the peripheral nervous system for prosthetic applications

The research presented in this thesis concerns the field of bioelectronics, in particular the work has been focused on the development of special electronic devices for neural signal acquisition and Peripheral Nervous System (PNS) stimulation. The final aim of the project in which this work is involved is in fact the realization of a prosthetic hand controlled using neural signals. The commercially available prosthesis are based on Electromyographic (EMG) signals, their use implies unnatural movements for the patient that needs a special training to develop the control capabilities over the mechanical limb. The proposed approach offers a number of advantages compared to the traditional prosthesis, first because the signals used are the same used to control the biologic limb, allowing a more comfortable solution for the patient that gets closer to feel the robotic hand as a natural extension of his/her body. Secondly, placing temperature and pressure sensors on the limb surface, it is possible to trasduce such information in an electrical current that, injected into the PNS, can restore the sensory feedback in amputees. The final goal of this research is the development of a fully implantable device able to perform a bidirectional communication between the robotic hand and the patient. Due to small area, low noise and low power constraints, the only possible way to reach this aim is the design of a full custom Integrated Circuit (IC). However a preliminary evaluation of the key design features, such as neural signal amplitudes and frequencies as well as stimulation shape parameters, is necessary in order to define clearly and precisely the design specifications. A low-cost and short implementation time device is then needed for this aim, the Components Off The Shelf (COTS) approach seems to be the best solution for this purpose. A Printed Circuit Board (PCB) with discrete components has been designed, developed and tested, the information extracted by the test results have been used to guide the IC design. The generation of electrical signals in biological cells, such as neural spikes, is possible thanks to ions that move across the cell membrane. In many applications it is important, not only to record the spikes, but also to measure these small currents in order to understand which electro-chemical processes are involved in the signal generation and to have a direct measurement of the ion channels involved in the reaction. Ion currents, in fact, play a key role in several physiological processes, in neural signal generation, but also in the maintenance of heartbeat and in muscle contraction. For this purpose, a system level implementation of a Read out circuit for ion channel current detection has been developed

Electronic bidirectional interfaces to the peripheral nervous system for prosthetic applications

The research presented in this thesis concerns the field of bioelectronics, in particular the work has been focused on the development of special electronic devices for neural signal acquisition and Peripheral Nervous System (PNS) stimulation. The final aim of the project in which this work is involved is in fact the realization of a prosthetic hand controlled using neural signals. The commercially available prosthesis are based on Electromyographic (EMG) signals, their use implies unnatural movements for the patient that needs a special training to develop the control capabilities over the mechanical limb. The proposed approach offers a number of advantages compared to the traditional prosthesis, first because the signals used are the same used to control the biologic limb, allowing a more comfortable solution for the patient that gets closer to feel the robotic hand as a natural extension of his/her body. Secondly, placing temperature and pressure sensors on the limb surface, it is possible to trasduce such information in an electrical current that, injected into the PNS, can restore the sensory feedback in amputees. The final goal of this research is the development of a fully implantable device able to perform a bidirectional communication between the robotic hand and the patient. Due to small area, low noise and low power constraints, the only possible way to reach this aim is the design of a full custom Integrated Circuit (IC). However a preliminary evaluation of the key design features, such as neural signal amplitudes and frequencies as well as stimulation shape parameters, is necessary in order to define clearly and precisely the design specifications. A low-cost and short implementation time device is then needed for this aim, the Components Off The Shelf (COTS) approach seems to be the best solution for this purpose. A Printed Circuit Board (PCB) with discrete components has been designed, developed and tested, the information extracted by the test results have been used to guide the IC design. The generation of electrical signals in biological cells, such as neural spikes, is possible thanks to ions that move across the cell membrane. In many applications it is important, not only to record the spikes, but also to measure these small currents in order to understand which electro-chemical processes are involved in the signal generation and to have a direct measurement of the ion channels involved in the reaction. Ion currents, in fact, play a key role in several physiological processes, in neural signal generation, but also in the maintenance of heartbeat and in muscle contraction. For this purpose, a system level implementation of a Read out circuit for ion channel current detection has been developed

Efficient Universal Computing Architectures for Decoding Neural Activity

The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain– machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain– machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than . We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA) implementation of this portion is consequently energy efficient. We validate the performance of our overall system by decoding electrophysiologic data from a behaving rodent.United States. National Institutes of Health (Grant NS056140

An implantable micro-system for neural prosthesis control and sensory feedback restoration in amputees

In this work, the prototype of an electronic bi-directional interface between the Peripheral Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is composed of two Integrated Circuits (ICs): a standard CMOS device for neural recording and a High Voltage (HV) CMOS device for neural stimulation. The integrated circuits have been realized in two different 0.35μm CMOS processes available fromAustriaMicroSystem(AMS). The recoding IC incorporates 8 channels each including the analog front-end and the A/D conversion based on a sigma delta architecture. It has a total area of 16.8mm2 and exhibits an overall power consumption of 27.2mW. The neural stimulation IC is able to provide biphasic current pulses to stimulate 8 electrodes independently. A voltage booster generates a 17V voltage supply in order to guarantee the programmed stimulation current even in case of high impedances at the electrode-tissue interface in the order of tens of k­. The stimulation patterns, generated by a 5-bit current DAC, are programmable in terms of amplitude, frequency and pulse width. Due to the huge capacitors of the implemented voltage boosters, the stimulation IC has a wider area of 18.6mm2. In addition, a maximum power consumption of 29mW was measured. Successful in-vivo experiments with rats having a TIME electrode implanted in the sciatic nerve were carried out, showing the capability of recording neural signals in the tens of microvolts, with a global noise of 7μVrms , and to selectively elicit the tibial and plantarmuscles using different active sites of the electrode. In order to get a completely implantable interface, a biocompatible and biostable package was designed. It hosts the developed ICs with the minimal electronics required for their proper operation. The package consists of an alumina tube closed at both extremities by two ceramic caps hermetically sealed on it. Moreover, the two caps serve as substrate for the hermetic feedthroughs to enable the device powering and data exchange with the external digital controller implemented on a Field-Programmable Gate Array (FPGA) board. The package has an outer diameter of 7mm and a total length of 26mm. In addition, a humidity and temperature sensor was also included inside the package to allow future hermeticity and life-time estimation tests. Moreover, a wireless, wearable and non-invasive EEG recording system is proposed in order to improve the control over the artificial limb,by integrating the neural signals recorded from the PNS with those directly acquired from the brain. To first investigate the system requirements, a Component-Off-The-Shelf (COTS) device was designed. It includes a low-power 8- channel acquisition module and a Bluetooth (BT) transceiver to transmit the acquired data to a remote platform. It was designed with the aimof creating a cheap and user-friendly system that can be easily interfaced with the nowadays widely spread smartphones or tablets by means of a mobile-based application. The presented system, validated through in-vivo experiments, allows EEG signals recording at different sample rates and with a maximum bandwidth of 524Hz. It was realized on a 19cm2 custom PCB with a maximum power consumption of 270mW

Glucose-powered neuroelectronics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-164).A holy grail of bioelectronics is to engineer biologically implantable systems that can be embedded without disturbing their local environments, while harvesting from their surroundings all of the power they require. As implantable electronic devices become increasingly prevalent in scientific research and in the diagnosis, management, and treatment of human disease, there is correspondingly increasing demand for devices with unlimited functional lifetimes that integrate seamlessly with their hosts in these two ways. This thesis presents significant progress toward establishing the feasibility of one such system: A brain-machine interface powered by a bioimplantable fuel cell that harvests energy from extracellular glucose in the cerebrospinal fluid surrounding the brain. The first part of this thesis describes a set of biomimetic algorithms and low-power circuit architectures for decoding electrical signals from ensembles of neurons in the brain. The decoders are intended for use in the context of neural rehabilitation, to provide paralyzed or otherwise disabled patients with instantaneous, natural, thought-based control of robotic prosthetic limbs and other external devices. This thesis presents a detailed discussion of the decoding algorithms, descriptions of the low-power analog and digital circuit architectures used to implement the decoders, and results validating their performance when applied to decode real neural data. A major constraint on brain-implanted electronic devices is the requirement that they consume and dissipate very little power, so as not to damage surrounding brain tissue. The systems described here address that constraint, computing in the style of biological neural networks, and using arithmetic-free, purely logical primitives to establish universal computing architectures for neural decoding. The second part of this thesis describes the development of an implantable fuel cell powered by extracellular glucose at concentrations such as those found in the cerebrospinal fluid surrounding the brain. The theoretical foundations, details of design and fabrication, mechanical and electrochemical characterization, as well as in vitro performance data for the fuel cell are presented.by Benjamin Isaac Rapoport.Ph.D

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

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

A Low-Power Wireless Multichannel Microsystem for Reliable Neural Recording.

This thesis reports on the development of a reliable, single-chip, multichannel wireless biotelemetry microsystem intended for extracellular neural recording from awake, mobile, and small animal models. The inherently conflicting requirements of low power and reliability are addressed in the proposed microsystem at architectural and circuit levels. Through employing the preliminary microsystems in various in-vivo experiments, the system requirements for reliable neural recording are identified and addressed at architectural level through the analytical tool: signal path co-optimization. The 2.85mm×3.84mm, mixed-signal ASIC integrates a low-noise front-end, programmable digital controller, an RF modulator, and an RF power amplifier (PA) at the ISM band of 433MHz on a single-chip; and is fabricated using a 0.5µm double-poly triple-metal n-well standard CMOS process. The proposed microsystem, incorporating the ASIC, is a 9-channel (8-neural, 1-audio) user programmable reliable wireless neural telemetry microsystem with a weight of 2.2g (including two 1.5V batteries) and size of 2.2×1.1×0.5cm3. The electrical characteristics of this microsystem are extensively characterized via benchtop tests. The transmitter consumes 5mW and has a measured total input referred voltage noise of 4.74µVrms, 6.47µVrms, and 8.27µVrms at transmission distances of 3m, 10m, and 20m, respectively. The measured inter-channel crosstalk is less than 3.5% and battery life is about an hour. To compare the wireless neural telemetry systems, a figure of merit (FoM) is defined as the reciprocal of the power spent on broadcasting one channel over one meter distance. The proposed microsystem’s FoM is an order of magnitude larger compared to all other research and commercial systems. The proposed biotelemetry system has been successfully used in two in-vivo neural recording experiments: i) from a freely roaming South-American cockroach, and ii) from an awake and mobile rat.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91542/1/aborna_1.pd