Neural Prostheses

Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.

The activity of motor cortex (MI) neurons conveys movement intent sufficiently well to be used as a control signal to operate artificial devices, but until now this has called for extensive training or has been confined to a limited movement repertoire. Here we show how activity from a few (7-30) MI neurons can be decoded into a signal that a monkey is able to use immediately to move a computer cursor to any new position in its workspace (14° × 14° visual angle). Our results, which are based on recordings made by an electrode array that is suitable for human use, indicate that neurally based control of movement may eventually be feasible in paralysed humans.

Electromyogram signals (EMGs) contain valuable information that can be used in man-machine interfacing between human users and myoelectric prosthetic devices. However, EMG signals are complicated and prove difficult to analyze due to physiological noise and other issues. Computational intelligence and machine learning techniques, such as artificial neural networks (ANNs), serve as powerful tools for analyzing EMG signals and creating optimal myoelectric control schemes for prostheses. This research examines the performance of four different neural network architectures (feedforward, recurrent, counter propagation, and self organizing map) that were tasked with classifying walking speed when given EMG inputs from 14 different leg muscles. Experiments conducted on the data set suggest that self organizing map neural networks are capable of classifying walking speed with greater than 99% accuracy.

Pulse Harmonic Modulation (PHM) is a novel pulse-based (carrierless) modulation method for wideband, low power data transmission across inductive telemetry links that operate in the near-field domain. PHM utilizes two or more unidentical pulses during each bit period to minimize inter-symbol interference (ISI). In this paper, we describe the PHM concept and demonstrate its operation with a proof-of-concept prototype, which achieves a data rate of 5.2 Mbps at 1 cm coil separation with a bit error rate (BER) of 10-6.

Background. The purpose of this experiment was to develop a peripheral nerve interface using cultured myoblasts within a scaffold to provide a biologically stable interface while providing signal amplification for neuroprosthetic control and preventing neuroma formation. Methods. A Regenerative Peripheral Nerve Interface (RPNI) composed of a scaffold and cultured myoblasts was implanted on the end of a divided peroneal nerve in rats (n = 25). The scaffold material consisted of either silicone mesh, acellular muscle, or acellular muscle with chemically polymerized poly(3,4-ethylenedioxythiophene) conductive polymer. Average implantation time was 93 days. Electrophysiological tests were performed at endpoint to determine RPNI viability and ability to transduce neural signals. Tissue samples were examined using both light microscopy and immunohistochemistry. Results. All implanted RPNIs, regardless of scaffold type, remained viable and displayed robust vascularity. Electromyographic ac...

Background. The purpose of this experiment was to develop a peripheral nerve interface using cultured myoblasts within a scaffold to provide a biologically stable interface while providing signal amplification for neuroprosthetic control and preventing neuroma formation. Methods. A Regenerative Peripheral Nerve Interface (RPNI) composed of a scaffold and cultured myoblasts was implanted on the end of a divided peroneal nerve in rats (n = 25). The scaffold material consisted of either silicone mesh, acellular muscle, or acellular muscle with chemically polymerized poly(3,4-ethylenedioxythiophene) conductive polymer. Average implantation time was 93 days. Electrophysiological tests were performed at endpoint to determine RPNI viability and ability to transduce neural signals. Tissue samples were examined using both light microscopy and immunohistochemistry. Results. All implanted RPNIs, regardless of scaffold type, remained viable and displayed robust vascularity. Electromyographic ac...

The gap between application specific integrated circuits (ASICs) and general-purpose programmable processors in terms of performance, power, cost and flexibility is well known. Application specific instruction-set processors (ASIPs) bridge this gap. In this work, we demonstrate the key benefits of ASIPs for several video applications. One of the most compute- and memory-intensive functions in video processing is motion estimation (ME). The focus of this work is on the design of a ME template, which is useful for several video applications like video encoding, obstacle detection, picturerate up-conversion, 2-D-to-3-D video conversion, etc. An instruction-set suitable for performing a variety of ME functions is developed. The ASIP is based on a very long instruction word (VLIW) processor template and meets low-power and low-cost requirements still providing the flexibility needed for the application domain. The ME ASIP design consumes 27 mW and takes an area of 1.1 mm2 in 0.13 μm technology performing picturerate up-conversion, for standard definition (CCIR601) resolution at 50 frames per second.

For rehabilitative devices to restore functional movement to paralyzed individuals, user intent must be determined from signals that remain under voluntary control. Tracking eye movements is a natural way to learn about an intended reach target and, when combined with just a small set of electromyograms (EMGs) in a probabilistic mixture model, can reliably generate accurate trajectories even when the target information is uncertain. To experimentally assess the effectiveness of our algorithm in closed-loop control, we developed a robotic system to simulate a reaching neuroprosthetic. Incorporating target information by tracking subjects' gaze greatly improved performance when the set of EMGs was most limited. In addition we found that online performance was better than predicted by the offline accuracy of the training data. By enhancing the trajectory model with target information the decoder relied less on neural control signals, reducing the burden on the user.

An implantable wireless system was developed for recording muscle afferent activity and stimulating peripheral nerves with cuff electrodes. The proposed system was fabricated into the nerve cuff electrode, neural amplifier, neural stimulator, and wireless communication system with battery power. The nerve cuff electrode and neural amplifier were designed to improve the signal-to-interference ratio and signal-to-noise ratio. The wireless communication system was designed based on the medical implant communication service regulations to be suitable for implantation. The main function of this system was to extract muscle afferent activity from peripheral nerve during functional electrical stimulation. The cuff electrodes were chronically implanted on the sciatic nerve for recording and on the tibial and peroneal nerves for stimulation. When the extension and flexion movements of ankle joint were elicited from alternative electrical stimuli, the corresponding neural signals and ankle an...

Lost sensations, such as touch, could one day be restored by electrical stimulation along the sensory neural pathways. Such stimulation, when informed by electronic sensors, could provide naturalistic cutaneous and proprioceptive feedback to the user. Perceptually, microstimulation of somatosensory brain regions produces localized, modality-specific sensations, and several spatiotemporal parameters have been studied for their discernibility. However, systematic methods for encoding a wide array of naturally occurring stimuli into biomimetic percepts via multi-channel microstimulation are lacking. More specifically, generating spatiotemporal patterns for explicitly evoking naturalistic neural activation has not yet been explored. We address this problem by first modeling the dynamical input-output relationship between multichannel microstimulation and downstream neural responses, and then optimizing the input pattern to reproduce naturally occurring touch responses as closely as poss...