A distributed, high-channel-count, implanted bidirectional system for restoration of somatosensation and myoelectric control.

Objective. We intend to chronically restore somatosensation and provide high-fidelity myoelectric control for those with limb loss via a novel, distributed, high-channel-count, implanted system.Approach.We have developed the implanted Somatosensory Electrical Neurostimulation and Sensing (iSens®) system to support peripheral nerve stimulation through up to 64, 96, or 128 electrode contacts with myoelectric recording from 16, 8, or 0 bipolar sites, respectively. The rechargeable central device has Bluetooth® wireless telemetry to communicate to external devices and wired connections for up to four implanted satellite stimulation or recording devices. We characterized the stimulation, recording, battery runtime, and wireless performance and completed safety testing to support its use in human trials.Results.The stimulator operates as expected across a range of parameters and can schedule multiple asynchronous, interleaved pulse trains subject to total charge delivery limits. Recorded signals in saline show negligible stimulus artifact when 10 cm from a 1 mA stimulating source. The wireless telemetry range exceeds 1 m (direction and orientation dependent) in a saline torso phantom. The bandwidth supports 100 Hz bidirectional update rates of stimulation commands and data features or streaming select full bandwidth myoelectric signals. Preliminary first-in-human data validates the bench testing result.Significance.We developed, tested, and clinically implemented an advanced, modular, fully implanted peripheral stimulation and sensing system for somatosensory restoration and myoelectric control. The modularity in electrode type and number, including distributed sensing and stimulation, supports a wide variety of applications; iSens® is a flexible platform to bring peripheral neuromodulation applications to clinical reality. ClinicalTrials.gov ID NCT04430218.

Restoring continuous finger function with temporarily paralyzed nonhuman primates using brain-machine interfaces.

Objective.Brain-machine interfaces (BMIs) have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user's own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand.Approach.We delivered a nerve block to the median, radial, and ulnar nerves just proximal to the elbow to simulate finger paralysis, then used a closed-loop BMI to predict finger movements the monkey was attempting to make in two tasks. The BCFES task was one-dimensional in which all fingers moved together, and we used the BMI's predictions to control FES of the monkey's finger muscles. The virtual two-finger task was two-dimensional in which the index finger moved simultaneously and independently from the middle, ring, and small fingers, and we used the BMI's predictions to control movements of virtual fingers, with no FES.Main results.In the BCFES task, the monkey improved his success rate to 83% (1.5 s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5 s median acquisition time, equal to the trial timeout) when attempting to use his temporarily paralyzed hand. In one monkey performing the virtual two-finger task with no FES, we found BMI performance (task success rate and completion time) could be completely recovered following temporary paralysis by executing recalibrated feedback-intention training one time.Significance.These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting factor in a BCFES neuroprosthesis.

Comparing different nonlinearities in readout systems for optical neuromorphic computing networks.

Nonlinear activation is a crucial building block of most machine-learning systems. However, unlike in the digital electrical domain, applying a saturating nonlinear function in a neural network in the analog optical domain is not as easy, especially in integrated systems. In this paper, we first investigate in detail the photodetector nonlinearity in two main readout schemes: electrical readout and optical readout. On a 3-bit-delayed XOR task, we show that optical readout trained with backpropagation gives the best performance. Furthermore, we propose an additional saturating nonlinearity coming from a deliberately non-ideal voltage amplifier after the detector. Compared to an all-optical nonlinearity, these two kinds of nonlinearities are extremely easy to obtain at no additional cost, since photodiodes and voltage amplifiers are present in any system. Moreover, not having to design ideal linear amplifiers could relax their design requirements. We show through simulation that for long-distance nonlinear fiber distortion compensation, using only the photodiode nonlinearity in an optical readout delivers BER improvements over three orders of magnitude. Combined with the amplifier saturation nonlinearity, we obtain another three orders of magnitude improvement of the BER.

Comparison of Myoelectric Control Schemes for Simultaneous Hand and Wrist Movement using Chronically Implanted Electromyography: A Case Series.

OBJECTIVE: A current biomedical engineering challenge is the development of a system that allows fluid control of multi-functional prosthetic devices through a human-machine interface. Here we probe this challenge by studying two subjects with trans-radial limb loss as they control a virtual hand and wrist system using 6 or 8 chronically implanted intramuscular electromyographic (iEMG) signals. The subjects successfully controlled a 4, 5, and 6 Degrees of Freedom (DoF's) virtual hand and wrist systems to perform a target matching task. APPROACH: Two control systems were evaluated where one tied EMG features directly to movement directions (Direct Control) and the other method determines user intent in the context of prior training data (Linear Interpolation). MAIN RESULTS: Subjects successfully matched most targets with both controllers but differences were seen as the complexity of the virtual limb system increased. The Direct Control method encountered difficulty due to crosstalk at higher DoF's. The Linear Interpolation method reduced crosstalk effects and outperformed Direct Control at higher DoF's. This work also studied the use of the Postural Control Algorithm to control the hand postures simultaneously with wrist degrees of freedom. SIGNIFICANCE: This work presents preliminary evidence that the PC algorithm can be used in conjunction with wrist control, that Direct Control with iEMG signals allows stable 4-DoF control, and that EMG pre-processing using the Linear Interpolation method can improve performance at 5 and 6-DoF's.

Stable, simultaneous and proportional 4-DoF prosthetic hand control via synergy-inspired linear interpolation: a case series.

BACKGROUND: Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control. METHODS: Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability. RESULTS AND CONCLUSIONS: In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times.

Design and Testing of Stimulation and Myoelectric Recording Modules in an Implanted Distributed Neuroprosthetic System.

Implantable motor neuroprostheses can restore functionality to individuals with neurological disabilities by electrically activating paralyzed muscles in coordinated patterns. The typical design of neuroprosthetic systems relies on a single multi-use device, but this limits the number of stimulus and sensor channels that can be practically implemented. To address this limitation, a modular neuroprosthesis, the "Networked Neuroprosthesis" (NNP), was developed. The NNP system is the first fully implanted modular neuroprosthesis that includes implantation of all power, signal processing, biopotential signal recording, and stimulating components. This paper describes the design of stimulation and recording modules, bench testing to verify stimulus outputs and appropriate filtering and recording, and validation that the components function properly while implemented in persons with spinal cord injury. The results of system testing demonstrated that the NNP was functional and capable of generating stimulus pulses and recording myoelectric, temperature, and accelerometer signals. Based on the successful design, manufacturing, and testing of the NNP System, multiple clinical applications are anticipated.

50 GBd PAM4 transmitter with a 55nm SiGe BiCMOS driver and silicon photonic segmented MZM.

We demonstrate an optical transmitter consisting of a limiting SiGe BiCMOS driver co-designed and co-packaged with a silicon photonic segmented traveling-wave Mach-Zehnder modulator (MZM). The MZM is split into two traveling-wave segments to increase the bandwidth and to allow a 2-bit DAC functionality. Two limiting driver channels are used to drive these segments, allowing both NRZ and PAM4 signal generation in the optical domain. The voltage swing as well as the peaking of the driver output are tunable, hence the PAM4 signal levels can be tuned and possible bandwidth limitations of the MZM segments can be partially alleviated. Generation of 50 Gbaud and 53 Gbaud PAM4 yields a TDECQ of 2.8 and 3.8 dB with a power efficiency of 3.9 and 3.6 pJ/bit, respectively; this is the best reported efficiency for co-packaged silicon transmitters for short-reach datacenter interconnects at these data rates. With this work, we show the potential of limiting drivers and segmented traveling-wave modulators in 400G capable short-reach optical interconnects.

Powering strategies for implanted multi-function neuroprostheses for spinal cord injury.

Implantable motor neuroprosthetic systems can restore function to individuals with significant disabilities, such as spinal cord injury, stroke, cerebral palsy, and multiple sclerosis. Neuroprostheses provide restored functionality by electrically activating paralysed muscles in coordinated patterns that replicate (enable) controlled movement that was lost through injury or disease. It is important to consider the general topology of the implanted system itself. The authors demonstrate that the wired multipoint implant technology is practical and feasible as a basis for the development of implanted multi-function neuroprosthetic systems. The advantages of a centralised power supply are significant. Heating due to recharge can be mitigated by using an actively cooled external recharge coil. Using this approach, the time required to perform a full recharge was significantly reduced. This approach has been demonstrated as a practical option for regular clinical use of implanted neuroprostheses.

4-channel 200 Gb/s WDM O-band silicon photonic transceiver sub-assembly.

We demonstrate a 200G capable WDM O-band optical transceiver comprising a 4-element array of Silicon Photonics ring modulators (RM) and Ge photodiodes (PD) co-packaged with a SiGe BiCMOS integrated driver and a SiGe transimpedance amplifier (TIA) chip. A 4×50 Gb/s data modulation experiment revealed an average extinction ratio (ER) of 3.17 dB, with the transmitter exhibiting a total energy efficiency of 2 pJ/bit. Data reception has been experimentally validated at 50 Gb/s per lane, achieving an interpolated 10E-12 bit error rate (BER) for an input optical modulation amplitude (OMA) of -9.5 dBm and a power efficiency of 2.2 pJ/bit, yielding a total power efficiency of 4.2 pJ/bit for the transceiver, including heater tuning requirements. This electro-optic subassembly provides the highest aggregate data-rate among O-band RM-based silicon photonic transceiver implementations, highlighting its potential for next generation WDM Ethernet transceivers.

Stable, three degree-of-freedom myoelectric prosthetic control via chronic bipolar intramuscular electrodes: a case study.

BACKGROUND: Modern prosthetic hands are typically controlled using skin surface electromyographic signals (EMG) from remaining muscles in the residual limb. However, surface electrode performance is limited by changes in skin impedance over time, day-to-day variations in electrode placement, and relative motion between the electrodes and underlying muscles during movement: these limitations require frequent retraining of controllers. In the presented study, we used chronically implanted intramuscular electrodes to minimize these effects and thus create a more robust prosthetic controller. METHODS: A study participant with a transradial amputation was chronically implanted with 8 intramuscular EMG electrodes. A K Nearest Neighbor (KNN) regression velocity controller was trained to predict intended joint movement direction using EMG data collected during a single training session. The resulting KNN was evaluated over 12 weeks and in multiple arm posture configurations, with the participant controlling a 3 Degree-of-Freedom (DOF) virtual reality (VR) hand to match target VR hand postures. The performance of this EMG-based controller was compared to a position-based controller that used movement measured from the participant's opposite (intact) hand. Surface EMG was also collected for signal quality comparisons. RESULTS: Signals from the implanted intramuscular electrodes exhibited less crosstalk between the various channels and had a higher Signal-to-Noise Ratio than surface electrode signals. The performance of the intramuscular EMG-based KNN controller in the VR control task showed no degradation over time, and was stable over the 6 different arm postures. Both the EMG-based KNN controller and the intact hand-based controller had 100% hand posture matching success rates, but the intact hand-based controller was slightly superior in regards to speed (trial time used) and directness of the VR hand control (path efficiency). CONCLUSIONS: Chronically implanted intramuscular electrodes provide negligible crosstalk, high SNR, and substantial VR control performance, including the ability to use a fixed controller over 12 weeks and under different arm positions. This approach can thus be a highly effective platform for advanced, multi-DOF prosthetic control.

Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

Transhumeral amputation has a significant effect on a person's independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person's proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.

Miniature low-power inertial sensors: promising technology for implantable motion capture systems.

Inertial and magnetic sensors are valuable for untethered, self-contained human movement analysis. Very recently, complete integration of inertial sensors, magnetic sensors, and processing into single packages, has resulted in miniature, low power devices that could feasibly be employed in an implantable motion capture system. We developed a wearable sensor system based on a commercially available system-in-package inertial and magnetic sensor. We characterized the accuracy of the system in measuring 3-D orientation-with and without magnetometer-based heading compensation-relative to a research grade optical motion capture system. The root mean square error was less than 4° in dynamic and static conditions about all axes. Using four sensors, recording from seven degrees-of-freedom of the upper limb (shoulder, elbow, wrist) was demonstrated in one subject during reaching motions. Very high correlation and low error was found across all joints relative to the optical motion capture system. Findings were similar to previous publications using inertial sensors, but at a fraction of the power consumption and size of the sensors. Such ultra-small, low power sensors provide exciting new avenues for movement monitoring for various movement disorders, movement-based command interfaces for assistive devices, and implementation of kinematic feedback systems for assistive interventions like functional electrical stimulation.

User-in-the-loop continuous and proportional control of a virtual prosthesis in a posture matching task.

As the development of dexterous prosthetic hand and wrist units continues, there is a need for command interfaces that will enable a user to operate these multi-joint devices in a natural, coordinated manner. In previous work, we have demonstrated that it is possible to simultaneously decode hand and wrist kinematics from myoelectric signals recorded from the forearm in an offline manner. The goal of this study was to quantify the performance of this command interface during real-time control of a kinematic prosthesis. One subject with intact limbs controlled a virtual prosthesis and attempted to match a series of target postures using the proposed control scheme as well as using the movements of the intact limb. Initial results indicate that subjects can complete these target matching tasks in the virtual environment. Future work will evaluate the controllability of the proposed strategy relative to traditional control schemes.

Electromyogram-based neural network control of transhumeral prostheses.

Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG) recording system with a wireless telemetry system that communicates with the patient's prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination) based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7° and 24.9° and average R(2) values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.

Virtual reality environment for simulating tasks with a myoelectric prosthesis: an assessment and training tool.

Intuitively and efficiently controlling multiple degrees of freedom is a major hurdle in the field of upper limb prosthetics. A virtual reality myoelectric transhumeral prosthesis simulator has been developed for cost-effectively testing novel control algorithms and devices. The system acquires EMG commands and residual limb kinematics, simulates the prosthesis dynamics, and displays the combined residual limb and prosthesis movements in a virtual reality environment that includes force-based interactions with virtual objects. A virtual Box and Block Test is demonstrated. Three normally-limbed subjects performed the simulated test using a sequential and a synchronous control method. With the sequential method, subjects moved on average 6.7±1.9 blocks in 120 seconds, similar to the number of blocks transhumeral amputees are able to move with their physical prostheses during clinical evaluation. With the synchronous method, subjects moved 6.7±2.2 blocks. The virtual reality prosthesis simulator is thus a promising tool for developing and evaluating control methods, prototyping novel prostheses, and training amputees.

Musculoskeletal model of trunk and hips for development of seated-posture-control neuroprosthesis.

The paralysis resulting from spinal cord injury severely limits voluntary seated-posture control and increases predisposition to a number of health risks. We developed and verified a musculoskeletal model of the hips and lumbar spine using published data. We then used the model to select the optimal muscles for-and evaluate the likely functional recovery benefit of-an 8-channel seated-posture-control neuroprosthesis based on functional electrical stimulation (FES). We found that the model-predicted optimal muscle set included the erector spinae, oblique abdominals, gluteus maximus, and iliopsoas. We mapped muscle excitations to seated trunk posture so that the required excitations at any posture could be approximated using a static map. Using the optimal muscle set, the model predicted a maximum stimulated range of motion of 49 degrees flexion, 9 degrees extension, and 16 degrees lateral bend. In the nominal upright posture, the modeled user could hold almost 15 kg with arms at sides and elbows bent. We discuss in this article the practicality of using FES with the oblique abdominals. A seated-posture-control neuroprosthesis would increase the user's bimanual work space and include several secondary benefits.