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Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.

Objective. Neural reflexes regulate immune responses and homeostasis. Advances in bioelectronic medicine indicate that electrical stimulation of the vagus nerve can be used to treat inflammatory disease, yet the understanding of neural signals that regulate inflammation is incomplete. Current interfaces with the vagus nerve do not permit effective chronic stimulation or recording in mouse models, which is vital to studying the molecular and neurophysiological mechanisms that control inflammation homeostasis in health and disease. We developed an implantable, dual purpose, multi-channel, flexible 'microelectrode' array, for recording and stimulation of the mouse vagus nerve. Approach. The array was microfabricated on an 8 µm layer of highly biocompatible parylene configured with 16 sites. The microelectrode was evaluated by studying the recording and stimulation performance. Mice were chronically implanted with devices for up to 12 weeks. Main results. Using the microelectrode in vivo, high fidelity signals were recorded during physiological challenges (e.g potassium chloride and interleukin-1β), and electrical stimulation of the vagus nerve produced the expected significant reduction of blood levels of tumor necrosis factor (TNF) in endotoxemia. Inflammatory cell infiltration at the microelectrode 12 weeks of implantation was limited according to radial distribution analysis of inflammatory cells. Significance. This novel device provides an important step towards a viable chronic interface for cervical vagus nerve stimulation and recording in mice.

A solid state radiation measurement system was designed and developed to handle extremely high flux (approaching 0.9Mcps) and a large dynamic range. To achieve this the system utilizes a CdZnTe crystal that has undergone prior high flux testing with high bias to ensure it will not exhibit significant depolarization that will limit its ability to measure high flux. In addition, a preamplifier that produces a very narrow pulse (~125ns FWHM), was designed and a high speed ADC was used to digitize the pulse. A Field Programmable Gate Array (FPGA) was implemented that allowed for scalable (2 or more channels counting asynchronously), high speed (130 MHz) pulse analysis in real time. The FPGA uses a pulse recognition algorithm to recognize pulses, even those beginning to pile up onto one another. This system was tested with a high energy (511keV) source that had an activity exceeding 10Ci.

This work was part of a pilot clinical study that involved the placement of an intracortical implant in the primary motor cortex of human subjects. The study, the first of its kind, involved the implantation of a 96 electrode array in the human brain for up to a full year. The work described here involved two of the subjects in the study, one of which was a stroke victim, and the other had ALS. Both subjects were tetraplegic and also unable to speak. The intent of this work was to determine if intended/imagined movements would evoke neural activity that could be successfully decoded and allow the subjects to control various devices in their environment and communicate through a personal computer. Neural decoding algorithms were developed to interpret the intracortical activity in the primary motor cortex of both subjects. Adaptive support vector machine methods were developed and employed during this study. A large set of intended/imagined movements was successfully decoded and discriminated between with the first subject. Also, both imagined shoulder and wrist movements were decoded in the second subject. Finally, the algorithms developed in this work allowed, for the first time, control of a motorized wheelchair through intended/imagined movements in a human with an intracortical electrode array implant.

There are numerous applications where a voice coil actuator is used for position control and it is not desirable to have a physical position sensor for feedback. This paper proposes a technique for sensorless position estimation of a linear voice coil transducer using sliding mode observers. The method exploits the fact that some voice coil designs possess position-dependent force and back-emf parameters due to their geometrical properties. Using an observer structure that incorporates these position-dependent parameters of the transducer allows the coil position to be observed from a current measurement. The nonlinear model developed for the voice coil is validated and experimental results are presented and discussed. Observability is proven and the results are incorporated into a sliding mode position feedback control algorithm.

An electromechanical model for a circular piezoelectric actuator is developed. A critical challenge in certain applications employing piezoceramic actuators is to maximize the displacement provided by the actuator while minimizing it power consumption. This problem is addressed here by developing an electromechanical model which can be used to optimize the volume displacement to admittance ratio for various circular actuator designs. The model includes multiple layers with independent radii which can be varied to optimize performance. The piezoceramic, bonding, plating, and mounting materials can be varied to accommodate various design criteria. An advantage of the model lies in the property that for a variety of material configurations, analytic solutions can be obtained. Numerical examples demonstrating the properties of the model are presented.

Robust vibration suppression (RVS) is a methodology developed for selecting and synthesizing preshapes that will reduce residual vibration in a wide variety of open and closed-loop multi-degree of freedom systems. Furthermore RVS requires only basic information about the system fundamental frequency and no information about higher modes in the system. There are many aspects to the RVS method; one particular RVS synthesis method called the binomial synthesis technique is discussed in this paper, and experimental data is presented for one RVS type preshape.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We present a novel 3D self-adaptive nerve electrode for high density nerve signal recording and site-specific stimulation. Specifically, a new pre-shaped flexible spiral structure has been developed in order to achieve tight contact with small nerves without any additional mechanical locking structure or force. This unique structure enables the nerve electrode to adapt and maintain close contact with the nerve without compressing it or restricting its movement. The spiral nerve electrodes (inner diameter = 310 μm) with 8 recording channels (electrode diameter = 50 μm) were fabricated and successfully applied to the rat vagus nerve (approximate diameter of 350 μm) in order to record compound action potentials.