Neural interfaces for control of upper limb prostheses: the state of the art and future possibilities

A World Update of Progress in Lower Extremity Transplantation: What's Hot and What's Not

ABSTRACT

The field of vascularized composite allotransplantation (VCA) is the new frontier of solid organ transplantation (SOT). VCA spans life-enhancing/life-changing procedures such as upper extremity, craniofacial (including eye), laryngeal, tracheal, abdominal wall, penis, and lower extremity transplants. VCAs such as uterus transplants are life giving unlike any other SOT. Of all VCAs that have shown successful intermediate- to long-term graft survival with functional and immunologic outcomes, lower extremity VCAs have remained largely underexplored. Lower extremity transplantation (LET) can offer patients with improved function compared to the use of conventional prostheses, reducing concerns of phantom limb pain and stump complications, and offer an option for eligible amputees that either fail prosthetic rehabilitation or do not adapt to prosthetics. Nevertheless, these benefits must be carefully weighed against the risks of VCA, which are not trivial, including the adverse effects of lifelong immunosuppression, extremely challenging perioperative care, and delayed nerve regeneration. There have been 5 lower extremity transplants to date, ranging from unilateral or bilateral to quadrimembral, progressively increasing in risk that resulted in fatalities in 3 of the 5 cases, emphasizing the inherent risks. The advantages of LET over prosthetics must be carefully weighed, demanding rigorous candidate selection for optimal outcomes.

Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review

Peripheral nerve interfaces (PNIs) are electrical systems designed to integrate with peripheral nerves in patients, such as following central nervous system (CNS) injuries to augment or replace CNS control and restore function. We review the literature for clinical trials and studies containing clinical outcome measures to explore the utility of human applications of PNIs. We discuss the various types of electrodes currently used for PNI systems and their functionalities and limitations. We discuss important design characteristics of PNI systems, including biocompatibility, resolution and specificity, efficacy, and longevity, to highlight their importance in the current and future development of PNIs. The clinical outcomes of PNI systems are also discussed. Finally, we review relevant PNI clinical trials that were conducted, up to the present date, to restore the sensory and motor function of upper or lower limbs in amputees, spinal cord injury patients, or intact individuals and describe their significant findings. This review highlights the current progress in the field of PNIs and serves as a foundation for future development and application of PNI systems.

Toward the Development of User-Centered Neurointegrated Lower Limb Prostheses

The last few years witnessed radical improvements in lower-limb prostheses. Researchers have presented innovative solutions to overcome the limits of the first generation of prostheses, refining specific aspects which could be implemented in future prostheses designs. Each aspect of lower-limb prostheses has been upgraded, but despite these advances, a number of deficiencies remain and the most capable limb prostheses fall far short of the capabilities of the healthy limb. This article describes the current state of prosthesis technology; identifies a number of deficiencies across the spectrum of lower limb prosthetic components with respect to users' needs; and discusses research opportunities in design and control that would substantially improve functionality concerning each deficiency. In doing so, the authors present a roadmap of patients related issues that should be addressed in order to fulfill the vision of a next-generation, neurally-integrated, highly-functional lower limb prosthesis.

An Artificial Olfactory System Based on a Chemi-Memristive Device

Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.

Soft Robotics Enables Neuroprosthetic Hand Design

Development and implementation of neuroprosthetic hands is a multidisciplinary field at the interface between humans and artificial robotic systems, which aims at replacing the sensorimotor function of the upper-limb amputees as their own. Although prosthetic hand devices with myoelectric control can be dated back to more than 70 years ago, their applications with anthropomorphic robotic mechanisms and sensory feedback functions are still at a relatively preliminary and laboratory stage. Nevertheless, a recent series of proof-of-concept studies suggest that soft robotics technology may be promising and useful in alleviating the design complexity of the dexterous mechanism and integration difficulty of multifunctional artificial skins, in particular, in the context of personalized applications. Here, we review the evolution of neuroprosthetic hands with the emerging and cutting-edge soft robotics, covering the soft and anthropomorphic prosthetic hand design and relating bidirectional neural interactions with myoelectric control and sensory feedback. We further discuss future opportunities on revolutionized mechanisms, high-performance soft sensors, and compliant neural-interaction interfaces for the next generation of neuroprosthetic hands.

Long-term upper-extremity prosthetic control using regenerative peripheral nerve interfaces and implanted EMG electrodes

Objective.Extracting signals directly from the motor system poses challenges in obtaining both high amplitude and sustainable signals for upper-limb neuroprosthetic control. To translate neural interfaces into the clinical space, these interfaces must provide consistent signals and prosthetic performance.Approach.Previously, we have demonstrated that the Regenerative Peripheral Nerve Interface (RPNI) is a biologically stable, bioamplifier of efferent motor action potentials. Here, we assessed the signal reliability from electrodes surgically implanted in RPNIs and residual innervated muscles in humans for long-term prosthetic control.Main results.RPNI signal quality, measured as signal-to-noise ratio, remained greater than 15 for up to 276 and 1054 d in participant 1 (P1), and participant 2 (P2), respectively. Electromyography from both RPNIs and residual muscles was used to decode finger and grasp movements. Though signal amplitude varied between sessions, P2 maintained real-time prosthetic performance above 94% accuracy for 604 d without recalibration. Additionally, P2 completed a real-world multi-sequence coffee task with 99% accuracy for 611 d without recalibration.Significance.This study demonstrates the potential of RPNIs and implanted EMG electrodes as a long-term interface for enhanced prosthetic control.

Prospective pre-operative 3-T MR neurography peripheral nerve mapping of upper extremity amputations implanted with FAST-LIFE electrode interfaces of robotic hands: technical report

BACKGROUND AND PURPOSE

Fascicular targeting of longitudinal intrafascicular electrode (FAST-LIFE) interface enables hand dexterity with exogenous electrical microstimulation for sensory restoration, custom neural recording hardware, and deep learning-based artificial intelligence for motor intent decoding. The purpose of this technical report from a prospective pilot study was to illustrate magnetic resonance neurography (MRN) mapping of hand and nerve anatomy in amputees and incremental value of MRN over electrophysiology findings in pre-surgical planning of FAST-LIFE interface (robotic hand) patients.

MATERIALS AND METHODS

After obtaining informed consent, patients with upper extremity amputations underwent pre-operative 3-T MRN, X-rays, and electrophysiology. MRN findings were correlated with electrophysiology reports. Descriptive statistics were performed.

RESULTS

Five patients of ages 21-59 years exhibited 3/5 partial hand amputations, and 2/5 transradial amputations on X-rays. The median and ulnar nerve end bulb neuromas measured 10.1 ± 3.04 mm (range: 5.5-14 mm, median: 10.5 mm) and 10.9 ± 7.64 mm (2-22 mm, 9.75 mm), respectively. The ADC of median and ulnar nerves were increased at 1.64 ± 0.1 × 10^-3 mm²/s (range: 1.5-1.8, median: 1.64 × 10^-3 mm²/s) and 1.70 ± 0.17 × 10^-3 mm²/s (1.49-1.98 × 10^-3 mm²/s, 1.65 × 10^-3 mm²/s), respectively. Other identified lesions were neuromas of superficial branch of the radial nerve and anterior interosseous nerve. On electrophysiology, 2/5 reports were unremarkable, 2/5 showed mixed motor-sensory neuropathies of median and ulnar nerves along with radial sensory neuropathy, and 1/5 showed sensory neuropathy of lateral cutaneous nerve of the forearm. All patients regained naturalistic sensations and motor control of digits.

CONCLUSION

3-T MRN allows excellent demonstration of forearm and hand nerve anatomy, altered diffusion characteristics, and their neuromas despite unremarkable electrophysiology for pre-surgical planning of the FAST-LIFE (robotic hand) interfaces.

An Analysis of Intention Detection Strategies to Control Advanced Assistive Technologies at the CYBATHLON

With the increasing range of functionalities of advanced assistive technologies (AAT), reliable control and initiation of the desired actions become increasingly challenging for users. In this work, we present an analysis of current practices, user preferences, and usability of AAT intention detection strategies based on a survey among participants with disabilities at the CYBATHLON 2020 Global Edition. We collected data from 35 respondents, using devices in various disciplines and levels of technology maturity. We found that conventional, direct inputs such as buttons and joysticks are used by the majority of AAT (71.4%) due to their simplicity and learnability. However, 22 respondents (62.8%) reported a desire for more natural control using muscle or non-invasive brain signals, and 37.1% even reported an openness to invasive strategies for potentially improved control. The usability of the used strategies in terms of the explored attributes (reliability, mental effort, required learning) was mainly perceived positively, whereas no significant difference was observed across intention detection strategies and device types. It can be assumed that the strategies used during the CYBATHLON realistically represent options to control an AAT in a dynamic, physically and mentally demanding environment. Thus, this work underlines the need for carefully considering user needs and preferences for the selection of intention detection strategies in a context of use outside the laboratory.

EMG-driven control in lower limb prostheses: a topic-based systematic review

BACKGROUND

The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts.

OBJECTIVE AND METHODOLOGIES

This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers.

RESULTS AND CONCLUSIONS

The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees.

Optimization of immune receptor-related hypersensitive cell death response assay using agrobacterium-mediated transient expression in tobacco plants

BACKGROUND

The study of the regulatory mechanisms of evolutionarily conserved Nucleotide-binding leucine-rich repeat (NLR) resistance (R) proteins in animals and plants is of increasing importance due to understanding basic immunity and the value of various crop engineering applications of NLR immune receptors. The importance of temperature is also emerging when applying NLR to crops responding to global climate change. In particular, studies of pathogen effector recognition and autoimmune activity of NLRs in plants can quickly and easily determine their function in tobacco using agro-mediated transient assay. However, there are conditions that should not be overlooked in these cell death-related assays in tobacco.

RESULTS

Environmental conditions play an important role in the immune response of plants. The system used in this study was to establish conditions for optimal hypertensive response (HR) cell death analysis by using the paired NLR RPS4/RRS1 autoimmune and AvrRps4 effector recognition system. The most suitable greenhouse temperature for growing plants was fixed at 22 °C. In this study, RPS4/RRS1-mediated autoimmune activity, RPS4 TIR domain-dependent cell death, and RPS4/RRS1-mediated HR cell death upon AvrRps4 perception significantly inhibited under conditions of 65% humidity. The HR is strongly activated when the humidity is below 10%. Besides, the leaf position of tobacco is important for HR cell death. Position #4 of the leaf from the top in 4-5 weeks old tobacco plants showed the most effective HR cell death.

CONCLUSIONS

As whole genome sequencing (WGS) or resistance gene enrichment sequencing (RenSeq) of various crops continues, different types of NLRs and their functions will be studied. At this time, if we optimize the conditions for evaluating NLR-mediated HR cell death, it will help to more accurately identify the function of NLRs. In addition, it will be possible to contribute to crop development in response to global climate change through NLR engineering.

The science and engineering behind sensitized brain-controlled bionic hands

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

Mechanical Design Optimization of Prosthetic Hand's Fingers: Novel Solutions towards Weight Reduction

From the mechanical function of grabbing objects to the emotional aspect of gesturing, the functionality of human hands is fundamental for both physical and social survival. Therefore, the loss of one or both hands represents a devastating issue, exacerbated by long rehabilitation times and psychological treatments. Prosthetic arms represent an effective solution to provide concrete functional and esthetical support. However, commercial hand prostheses still lack an optimal combination of light weight, durability, adequate cosmetic appearance, and affordability. Among these aspects, the priority for upper-limb prosthesis users is weight, a key parameter that influences both the portability and the functionality of the system. The purpose of this work is to optimize the design of the MyHand prosthesis, by redesigning both the proximal and distal finger and thumb in light of finding an optimal balance between weight reduction and adequate stiffness. Starting from elastic–plastic numerical models and experimental tests on obsolete components, analyzed under the worst loading condition, five different design solutions are suggested. An iterative topology optimization process locates the regions where material removal is permitted. From these results, 2 mm geometrical patterns on the top surface of the hand prosthesis appear as the most prominent, preventing object intrusion.

Sensory Feedback for Upper-Limb Prostheses: Opportunities and Barriers

The addition of sensory feedback to upper-limb prostheses has been shown to improve control, increase embodiment, and reduce phantom limb pain. However, most commercial prostheses do not incorporate sensory feedback due to several factors. This paper focuses on the major challenges of a lack of deep understanding of user needs, the unavailability of tailored, realistic outcome measures and the segregation between research on control and sensory feedback. The use of methods such as the Person-Based Approach and co-creation can improve the design and testing process. Stronger collaboration between researchers can integrate different prostheses research areas to accelerate the translation process.

A Residual Movement Classification Based User Interface for Control of Assistive Devices by Persons with Complete Tetraplegia

OBJECTIVE

Complete tetraplegia can deprive a person of hand function. Assistive technologies may improve autonomy but needs for ergonomic interfaces for the user to pilot these devices still persist. Despite the paralysis of their arms, people with tetraplegia may retain residual shoulder movements. In this work we explored these movements as a mean to control assistive devices.

METHODS

We captured shoulder movement with a single inertial sensor and, by training a support vector machine based classifier, we decode such information into user intent.

RESULTS

The setup and training process take only a few minutes and so the classifiers can be user specific. We tested the algorithm with 10 able body and 2 spinal cord injury participants. The average classification accuracy was 80% and 84%, respectively.

CONCLUSION

The proposed algorithm is easy to set up, its operation is fully automated, and achieved results are on par with state-of-the-art systems.

SIGNIFICANCE

Assistive devices for persons without hand function present limitations in their user interfaces. Our work present a novel method to overcome some of these limitations by classifying user movement and decoding it into user intent, all with simple setup and training and no need for manual tuning. We demonstrate its feasibility with experiments with end users, including persons with complete tetraplegia without hand function.

Nanofibrous PEDOT-Carbon Composite on Flexible Probes for Soft Neural Interfacing

In this study, we report a flexible implantable 4-channel microelectrode probe coated with highly porous and robust nanocomposite of poly (3,4-ethylenedioxythiophene) (PEDOT) and carbon nanofiber (CNF) as a solid doping template for high-performance in vivo neuronal recording and stimulation. A simple yet well-controlled deposition strategy was developed via in situ electrochemical polymerization technique to create a porous network of PEDOT and CNFs on a flexible 4-channel gold microelectrode probe. Different morphological and electrochemical characterizations showed that they exhibit remarkable and superior electrochemical properties, yielding microelectrodes combining high surface area, low impedance (16.8 ± 2 MΩ µm² at 1 kHz) and elevated charge injection capabilities (7.6 ± 1.3 mC/cm²) that exceed those of pure and composite PEDOT layers. In addition, the PEDOT-CNF composite electrode exhibited extended biphasic charge cycle endurance and excellent performance under accelerated lifetime testing, resulting in a negligible physical delamination and/or degradation for long periods of electrical stimulation. In vitro testing on mouse brain slices showed that they can record spontaneous oscillatory field potentials as well as single-unit action potentials and allow to safely deliver electrical stimulation for evoking field potentials. The combined superior electrical properties, durability and 3D microstructure topology of the PEDOT-CNF composite electrodes demonstrate outstanding potential for developing future neural surface interfacing applications.

Estimation of Lower Limb Kinematics during Squat Task in Different Loading Using sEMG Activity and Deep Recurrent Neural Networks

The aim of the present study was to predict the kinematics of the knee and the ankle joints during a squat training task of different intensities. Lower limb surface electromyographic (sEMG) signals and the 3-D kinematics of lower extremity joints were recorded from 19 body builders during squat training at four loading conditions. A long-short term memory (LSTM) was used to estimate the kinematics of the knee and the ankle joints. The accuracy, in terms root-mean-square error (RMSE) metric, of the LSTM network for the knee and ankle joints were 6.774 ± 1.197 and 6.961 ± 1.200, respectively. The LSTM network with inputs processed by cross-correlation (CC) method showed 3.8% and 4.7% better performance in the knee and ankle joints, respectively, compared to when the CC method was not used. Our results showed that in the prediction, regardless of the intensity of movement and inter-subject variability, an off-the-shelf LSTM decoder outperforms conventional fully connected neural networks.

Long-termin-vivorecording performance of flexible penetrating microelectrode arrays

Objective. Neural interfaces are an essential tool to enable the human body to directly communicate with machines such as computers or prosthetic robotic arms. Since invasive electrodes can be located closer to target neurons, they have advantages such as precision in stimulation and high signal-to-noise ratio (SNR) in recording, while they often exhibit unstable performance in long-termin-vivoimplantation because of the tissue damage caused by the electrodes insertion. In the present study, we investigated the electrical functionality of flexible penetrating microelectrode arrays (FPMAs) up to 3 months inin-vivoconditions.Approach. Thein-vivoexperiment was performed by implanting FPMAs in five rats. Thein-vivoimpedance as well as the action potential (AP) amplitude and SNR were analyzed over weeks. Additionally, APs were tracked over time to investigate the possibility of single neuron recording.Main results. It was observed that the FPMAs exhibited dramatic increases in impedance for the first 4 weeks after implantation, accompanied by decreases in AP amplitude. However, the increase/decrease in AP amplitude was always accompanied by the increase/decrease in background noise, resulting in quite consistently maintained SNRs. After 4 weeks of implantation, we observed two distinctive issues regarding long-term implantation, each caused by chronic tissue responses or by the delamination of insulation layer. The results demonstrate that the FPMAs successfully recorded neuronal signals up to 12 weeks, with very stably maintained SNRs, reduced by only 16.1% on average compared to the first recordings, although biological tissue reactions or physical degradation of the FPMA were present.Significance. The fabricated FPMAs successfully recorded intracortical signals for 3 months. The SNR was maintained up to 3 months and the chronic function of FPMA was comparable with other silicon based implantable electrodes.

Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions

Objective.Advanced robotic lower limb prostheses are mainly controlled autonomously. Although the existing control can assist cyclic movements during locomotion of amputee users, the function of these modern devices is still limited due to the lack of neuromuscular control (i.e. control based on human efferent neural signals from the central nervous system to peripheral muscles for movement production). Neuromuscular control signals can be recorded from muscles, called electromyographic (EMG) or myoelectric signals. In fact, using EMG signals for robotic lower limb prostheses control has been an emerging research topic in the field for the past decade to address novel prosthesis functionality and adaptability to different environments and task contexts. The objective of this paper is to review robotic lower limb Prosthesis control via EMG signals recorded from residual muscles in individuals with lower limb amputations.Approach.We performed a literature review on surgical techniques for enhanced EMG interfaces, EMG sensors, decoding algorithms, and control paradigms for robotic lower limb prostheses.Main results.This review highlights the promise of EMG control for enabling new functionalities in robotic lower limb prostheses, as well as the existing challenges, knowledge gaps, and opportunities on this research topic from human motor control and clinical practice perspectives.Significance.This review may guide the future collaborations among researchers in neuromechanics, neural engineering, assistive technologies, and amputee clinics in order to build and translate true bionic lower limbs to individuals with lower limb amputations for improved motor function.

Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals

Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a “pseudo-online” dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.

Brain-machine and muscle-machine bio-sensing methods for gesture intent acquisition in upper-limb prosthesis control: a review

This paper presents a review of a number of bio-sensing methods for gesture intent signal acquisition in control tasks for upper-limb prosthesis. The paper specifically provides a breakdown of the control task in myoelectric prosthesis, and in addition, highlights and describes the importance of the acquisition of a high-quality bio-signal. The paper also describes commonly used invasive and non-invasive brain and muscle machine interfaces such as electroencephalography, electrocorticography, electroneurography, surface electromyography, sonomyography, mechanomyography, near infra-red, force sensitive resistance/pressure, and magnetoencephalography. Each modality is reviewed based on its operating principle and limitations in gesture recognition, followed by respective advantages and disadvantages. Also described within this paper, are multimodal sensing approaches, which involve data fusion of information from various sensing modalities for an enhanced neuromuscular bio-sensing source. Using a semi-systematic review methodology, we are able to derive a novel tabular approach towards contrasting the various strengths and weaknesses of the reviewed bio-sensing methods towards gesture recognition in a prosthesis interface. This would allow for a streamlined method of down selection of an appropriate bio-sensor given specific prosthesis design criteria and requirements. The paper concludes by highlighting a number of research areas that require more work for strides to be made towards improving and enhancing the connection between man and machine as it concerns upper-limb prosthesis. Such areas include classifier augmentation for gesture recognition, filtering techniques for sensor disturbance rejection, feeling of tactile sensations with an artificial limb.

Advances in Implantable Microelectrode Array Insertion and Positioning

OBJECTIVES

Microelectrode arrays offer a means to probe the functional circuitry of the brain and the promise of cortical neuroprosthesis for individuals suffering from paralysis or limb loss. These devices are typically comprised of one or more shanks incorporating microelectrode sites, where the shanks are positioned by inserting the devices along a straight path that is normal to the brain surface. The lack of consistent long-term chronic recording technology has driven interest in novel probe design and approaches that go beyond the standard insertion approach that is limited to a single velocity or axis. This review offers a description of typical approaches and associated limitations and surveys emergent methods for implantation of microelectrode arrays, in particular those new approaches that leverage embedded microactuators and extend the insertion direction beyond a single axis.

MATERIALS AND METHODS

This review paper surveys the current technologies that enable probe implantation, repositioning, and the capability to record/stimulate from a tissue volume. A comprehensive literature search was performed using PubMed, Web of Science, and Google Scholar.

RESULTS

There has been substantial innovation in the development of microscale and embedded technology that enables probe repositioning to maintain quality recordings in the brain. Innovations in material science have resulted in novel strategies for deployable structures that can record from or stimulate a tissue volume. Moreover, new developments involving magnetically steerable catheters and needles offer an alternative approach to "pull" rather than "push" a probe into the tissue.

CONCLUSION

We envision the emergence of a new generation of probes and insertion methodologies for neuromodulation applications that enable reliable chronic performance from devices that can be positioned virtually anywhere in the brain.

On prosthetic control: A regenerative agonist-antagonist myoneural interface

Prosthetic limb control is fundamentally constrained by the current amputation procedure. Since the U.S. Civil War, the external prosthesis has benefited from a pronounced level of innovation, but amputation technique has not significantly changed. During a standard amputation, nerves are transected without the reintroduction of proper neural targets, causing painful neuromas and rendering efferent recordings infeasible. Furthermore, the physiological agonist-antagonist muscle relationships are severed, precluding the generation of musculotendinous proprioception, an afferent feedback modality critical for joint stability, trajectory planning, and fine motor control. We establish an agonist-antagonist myoneural interface (AMI), a unique surgical paradigm for amputation. Regenerated free muscle grafts innervated with transected nerves are linked in agonist-antagonist relationships, emulating the dynamic interactions found within an intact limb. Using biomechanical, electrophysiological, and histological evaluations, we demonstrate a viable architecture for bidirectional signaling with transected motor nerves. Upon neural activation, the agonist muscle contracts, generating electromyographic signal. This contraction in the agonist creates a stretch in the mechanically linked antagonist muscle, producing afferent feedback, which is transmitted through its motor nerve. Histological results demonstrate regeneration and the presence of the spindle fibers responsible for afferent signal generation. These results suggest that the AMI will not only produce robust signals for the efferent control of an external prosthesis but also provide an amputee's central nervous system with critical musculotendinous proprioception, offering the potential for an enhanced prosthetic controllability and sensation.

A bioelectric neural interface towards intuitive prosthetic control for amputees

OBJECTIVE

While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful.

APPROACH

Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject's motor intention.

MAIN RESULTS

A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient's true intention.

SIGNIFICANCE

Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways. (Clinical trial identifier: NCT02994160).

Novel approach for electromyography-controlled prostheses based on facial action

Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications. Graphical abstract Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications.

Development of Soft sEMG Sensing Structures Using 3D-Printing Technologies

3D printing of soft EMG sensing structures enables the creation of personalized sensing structures that can be potentially integrated in prosthetic, assistive and other devices. We developed and characterized flexible carbon-black doped TPU-based sEMG sensing structures. The structures are directly 3D-printed without the need for an additional post-processing step using a low-cost, consumer grade multi-material FDM printer. A comparison between the gold standard Ag/AgCl gel electrodes and the 3D-printed EMG electrodes with a comparable contact area shows that there is no significant difference in the EMG signals' amplitude. The sensors are capable of distinguishing a variable level of muscle activity of the biceps brachii. Furthermore, as a proof of principle, sEMG data of a 3D-printed 8-electrode band are analyzed using a patten recognition algorithm to recognize hand gestures. This work shows that 3D-printed sEMG electrodes have great potential in practical applications.

Nanotunnels within Poly(3,4-ethylenedioxythiophene)-Carbon Nanotube Composite for Highly Sensitive Neural Interfacing

Neural electrodes are developed for direct communication with neural tissues for theranostics. Although various strategies have been employed to improve performance, creating an intimate electrode-tissue interface with high electrical fidelity remains a great challenge. Here, we report the rational design of a tunnel-like electrode coating comprising poly(3,4-ethylenedioxythiophene) (PEDOT) and carbon nanotubes (CNTs) for highly sensitive neural recording. The coated electrode shows a 50-fold reduction in electrochemical impedance at the biologically relevant frequency of 1 kHz, compared to the bare gold electrode. The incorporation of CNT significantly reinforces the nanotunnel structure and improves coating adhesion by ∼1.5 fold. In vitro primary neuron culture confirms an intimate contact between neurons and the PEDOT-CNT nanotunnel. During acute in vivo nerve recording, the coated electrode enables the capture of high-fidelity neural signals with low susceptibility to electrical noise and reveals the potential for precisely decoding sensory information through mechanical and thermal stimulation. These findings indicate that the PEDOT-CNT nanotunnel composite serves as an active interfacing material for neural electrodes, contributing to neural prosthesis and brain-machine interface.

A Review of Sensory Feedback in Upper-Limb Prostheses From the Perspective of Human Motor Control

This manuscript reviews historical and recent studies that focus on supplementary sensory feedback for use in upper limb prostheses. It shows that the inability of many studies to speak to the issue of meaningful performance improvements in real-life scenarios is caused by the complexity of the interactions of supplementary sensory feedback with other types of feedback along with other portions of the motor control process. To do this, the present manuscript frames the question of supplementary feedback from the perspective of computational motor control, providing a brief review of the main advances in that field over the last 20 years. It then separates the studies on the closed-loop prosthesis control into distinct categories, which are defined by relating the impact of feedback to the relevant components of the motor control framework, and reviews the work that has been done over the last 50+ years in each of those categories. It ends with a discussion of the studies, along with suggestions for experimental construction and connections with other areas of research, such as machine learning.

The future of upper extremity rehabilitation robotics: research and practice

The loss of upper limb motor function can have a devastating effect on people's lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body's motor system. In this invited review, we aim to provide details on the peripheral nerve interfaces and brain-machine interfaces that have been developed in the past 30 years for upper extremity control, and we highlight the challenges that still remain to transition the technology into the clinical market. The findings show that peripheral nerve interfaces and brain-machine interfaces have many similar characteristics that enable them to be concurrently developed. Decoding neural information from both interfaces may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.

Targeted Muscle Reinnervation Combined with a Vascularized Pedicled Regenerative Peripheral Nerve Interface

Symptomatic neuromas and pain caused by nerve transection injuries can adversely impact a patient’s recovery, while also contributing to increased dependence on opioid and other pharmacotherapy. These sources of pain are magnified following amputation surgeries, inhibiting optimal prosthetic wear and function. Targeted muscle reinnervation (TMR) and regenerative peripheral nerve interfaces (RPNI) represent modern advances in addressing amputated peripheral nerves. These techniques offer solutions by essentially providing neuromuscular targets for transected peripheral nerves “to grow into and reinnervate.” Recent described benefits of these techniques include reports on pain reduction or ablation (eg, phantom limb pain, residual limb pain, and/or neuroma pain).^1–6 We describe a technical adaptation combining TMR with a “pedicled vascularized RPNI (vRPNI).” The TMR with the vRPNI surgical technique described offers the advantage of having a distal target nerve and a target muscle possessing deinnervated motor end plates which may potentially enhance nerve regeneration and muscle reinnervation, while also decreasing amputated nerve-related pain.

Forequarter Amputation and Reconstructive Options

OBJECTIVE

This study aimed to present the results of a series of forequarter amputations (FQAs) and to evaluate the reconstructive methods used.

SUMMARY BACKGROUND DATA

Although FQA has become a rare procedure in the era of limb-sparing treatment of extremity malignancies, it is a useful option when resection of a shoulder girdle or proximal upper extremity tumor cannot be performed so as to retain a functional limb.

METHODS

Thirty-four patients were treated with FQA in 1989 to 2017. Various reconstructive techniques were used, including free fillet flaps from the amputated extremity.

RESULTS

All patients presented with intractable symptoms such as severe pain, motor or sensory deficit, or limb edema. Seventeen patients were treated with palliative intent. Chest wall resection was performed in 9 patients. Free flap reconstruction was necessary for 15 patients, with 11 free flaps harvested from the amputated extremity. There was no operative mortality, and no free flaps were lost. In curatively treated patients, estimated 5-year disease-specific survival was 60%. Median survival in the palliatively treated group was 13 months (1-35 months).

CONCLUSIONS

Limb-sparing treatment is preferable for most shoulder girdle and proximal upper extremity tumors. Sometimes, FQA is the only option enabling curative treatment. In palliative indications, considerable disease-free intervals and relief from disabling symptoms can be achieved. The extensive tissue defects caused by extended FQA can be safely and reliably reconstructed by means of free flaps, preferably harvested from the amputated extremity.

Neural signal recording and processing in somatic neuroprosthetic applications. A review

Neurointerfaces have acquired major relevance as both rehabilitative and therapeutic tools for patients with spinal cord injury, limb amputations and other neural disorders. Bidirectional neural interfaces are a key component for the functional control of neuroprosthetic devices. The two main neuroprosthetic applications of interfaces with the peripheral nervous system (PNS) are: the refined control of artificial prostheses with sensory neural feedback, and functional electrical stimulation (FES) systems attempting to generate motor or visceral responses in paralyzed organs. The results obtained in experimental and clinical studies with both, extraneural and intraneural electrodes are very promising in terms of the achieved functionality for the neural stimulation mode. However, the results of neural recordings with peripheral nerve interfaces are more limited. In this paper we review the different existing approaches for PNS signals recording, denoising, processing and classification, enabling their use for bidirectional interfaces. PNS recordings can provide three types of signals: i) population activity signals recorded by using extraneural electrodes placed on the outer surface of the nerve, which carry information about cumulative nerve activity; ii) spike activity signals recorded with intraneural electrodes placed inside the nerve, which carry information about the electrical activity of a set of individual nerve fibers; and iii) hybrid signals, which contain both spiking and cumulative signals. Finally, we also point out some of the main limitations, which are hampering clinical translation of neural decoding, and indicate possible solutions for improvement.

Acute in vivo testing of a polymer cuff electrode with integrated microfluidic channels for stimulation, recording, and drug delivery on rat sciatic nerve

BACKGROUND

Extraneural cuffs are among the least invasive peripheral nerve interfaces as they remain outside the nerve. However, compared with more invasive interfaces, these electrodes may suffer from lower selectivity and sensitivity since the targeted nerve fibers are more distanced from the electrodes.

NEW METHOD

A lyse-and-attract cuff electrode (LACE) was enabled by microfabrication and developed to improve selectivity and sensitivity while maintaining a cuff format. Its engineering design was described in previous work. LACE is a hybrid cuff that integrates both microelectrodes and microfluidic channels. The ultimate goal is to increase fascicular selectivity and sensitivity by focal delivery via the microchannels of (1) lysing agent to remove connective tissue separating electrodes from nerve fibers, and (2) neurotrophic factors to promote axonal sprouting of the exposed nerve fibers into microfluidic channels where electrodes are embedded. Here, we focus on demonstrating in vivo function of microfluidics and microelectrodes in an acute preparation in which we evaluate the ability to focally remove connective tissue and record and stimulate with microchannel-embedded microelectrodes neural activity in rat sciatic nerves.

COMPARISON WITH EXISTING METHODS

While extraneural interfaces prioritize nerve health and intraneural interfaces prioritize functionality, LACE represents a new extraneural approach which could potentially excel at both aims.

RESULTS

Surgical implantation demonstrate preservation of LACE function following careful and minimal handling. In vivo electrical evaluation demonstrates the ability of microelectrodes placed within microfluidic channels to successfully stimulate and record compound action potentials from rat sciatic nerve. Furthermore, collagen-rich epineurium was focally removed following infusion of collagenase via microchannels and confirmed via microscopy.

CONCLUSION

The feasibility of using a cuff having integrated microelectrodes and microfluidics to stimulate, record, and deliver drug to focally lyse away the epineurium layer was demonstrated in acute experiments on rat sciatic nerve.

A review for the peripheral nerve interface designer

Informational density and relative accessibility of the peripheral nervous system make it an attractive site for therapeutic intervention. Electrode-based electrophysiological interfaces with peripheral nerves have been under development since the 1960s and, for several applications, have seen widespread clinical implementation. However, many applications require a combination of neural target resolution and stability which has thus far eluded existing peripheral nerve interfaces (PNIs). With the goal of aiding PNI designers in development of devices that meet the demands of next-generation applications, this review seeks to collect and present practical considerations and best practices which emerge from the literature, including both lessons learned during early PNI development and recent ideas. Fundamental and practical principles guiding PNI design are reviewed, followed by an updated and critical account of existing PNI designs and strategies. Finally, a brief survey of in vitro and in vivo PNI characterization methods is presented.

Assisted Grasping in Individuals with Tetraplegia: Improving Control through Residual Muscle Contraction and Movement

Individuals who sustained a spinal cord injury often lose important motor skills, and cannot perform basic daily living activities. Several assistive technologies, including robotic assistance and functional electrical stimulation, have been developed to restore lost functions. However, designing reliable interfaces to control assistive devices for individuals with C4–C8 complete tetraplegia remains challenging. Although with limited grasping ability, they can often control upper arm movements via residual muscle contraction. In this article, we explore the feasibility of drawing upon these residual functions to pilot two devices, a robotic hand and an electrical stimulator. We studied two modalities, supra-lesional electromyography (EMG), and upper arm inertial sensors (IMU). We interpreted the muscle activity or arm movements of subjects with tetraplegia attempting to control the opening/closing of a robotic hand, and the extension/flexion of their own contralateral hand muscles activated by electrical stimulation. Two groups were recruited: eight subjects issued EMG-based commands; nine other subjects issued IMU-based commands. For each participant, we selected at least two muscles or gestures detectable by our algorithms. Despite little training, all participants could control the robot’s gestures or electrical stimulation of their own arm via muscle contraction or limb motion.

Users' and therapists' perceptions of myoelectric multi-function upper limb prostheses with conventional and pattern recognition control

Objective

To describe users’ and therapists’ opinions on multi-function myoelectric upper limb prostheses with conventional control and pattern recognition control.

Design

Qualitative interview study.

Settings

Two rehabilitation institutions in the Netherlands and one in Austria.

Subjects

The study cohort consisted of 15 prosthesis users (13 males, mean age: 43.7 years, average experience with multi-function prosthesis: 3.15 years) and seven therapists (one male, mean age: 44.1 years, average experience with multi-function prostheses: 6.6 years). Four of these users and one therapist had experience with pattern recognition control.

Method

This study consisted of semi-structured interviews. The participants were interviewed at their rehabilitation centres or at home by telephone. The thematic framework approach was used for analysis.

Results

The themes emerging from prosthesis users and therapists were largely congruent and resulted in one thematic framework with three main themes: control, prosthesis, and activities. The participants mostly addressed (dis-) satisfaction with the control type and the prosthesis itself and described the way they used their prostheses in daily tasks.

Conclusion

Prosthesis users and therapists described multi-function upper limb prostheses as more functional devices than conventional one-degree-of-freedom prostheses. Nonetheless, the prostheses were seldom used to actively grasp and manipulate objects. Moreover, the participants clearly expressed their dissatisfaction with the mechanical robustness of the devices and with the process of switching prosthesis function under conventional control. Pattern recognition was appreciated as an intuitive control that facilitated fast switching between prosthesis functions, but was reported to be too unreliable for daily use and require extensive training.

Precise Tubular Braid Structures of Ultrafine Microwires as Neural Probes: Significantly Reduced Chronic Immune Response and Greater Local Neural Survival in Rat Cortex

Braided multi-electrode probes (BMEPs) for neural interfaces comprise ultrafine microwire bundles interwoven into tubular braids. BMEPs provide highly flexible probes and tethers, and an open lattice structure with up to 24 recording/stimulating channels in precise geometries, currently all within a [Formula: see text] diameter footprint. This paper compares the long-term tissue effects of BMEPs ( [Formula: see text] wires) versus single conventional 50- [Formula: see text] wires, by testing nearby chronic immune response and neural survival in rat cortex. Four different types of electrodes were implanted in cortex in each of eight rats: 1) BMEP with tether; 2) tethered 50- [Formula: see text] wire; 3) BMEP without a tether; and 4) untethered 50- [Formula: see text] wire. Quantitative immunohistological statistical comparisons after eight weeks using GFAP, ED1, and NeuN staining clearly showed that both BMEP implants had significantly less tissue immune response and more neuronal survival than either of the 50- [Formula: see text] wires ( ) in each of the eight rats. Data strongly indicate that BMEP tissue responses are superior, and that BMEP designs partly alleviate chronic tissue inflammatory responses and neural losses. The flexible body, tether and open braid lattice, and finer wire diameters of BMEP designs may all contribute to reducing the biological long-term response.

A Novel Muscle Transfer for Independent Digital Control of a Myoelectric Prosthesis: The Starfish Procedure

Control of independent digital flexion and extension has remained an elusive goal in myoelectric prosthetics for upper extremity amputees. We first performed a cadaver study to determine the feasibility of transferring the interossei muscles for each digit to the dorsum of the hand without damaging the neurovascular pedicles. Once this capability was ensured, a clinical case was performed transferring the interossei of the middle and ring fingers to the dorsum of the hand where they could serve as a myoelectric signal for a partial hand amputee to allow individual digital control with a myoelectric prosthesis. Before surgery, it was impossible to detect an independent signal for each interossei; however, after the surgery, signals were reliably detected, which allowed these muscles to serve as myosites for finger flexion using a myoelectric prosthesis and move each digit independently. This concept of salvaging innervated and perfused muscles from an amputated part and transferring them into the more proximal and superficial portion of a salvaged limb has broad applications for improved myoelectric prosthetic control.

Identification of forearm skin zones with similar muscle activation patterns during activities of daily living

Background

A deeper knowledge of the activity of the forearm muscles during activities of daily living (ADL) could help to better understand the role of those muscles and allow clinicians to treat motor dysfunctions more effectively and thus improve patients’ ability to perform activities of daily living.

Methods

In this work, we recorded sEMG activity from 30 spots distributed over the skin of the whole forearm of six subjects during the performance of 21 representative simulated ADL from the Sollerman Hand Function Test. Functional principal component analysis and hierarchical cluster analysis (HCA) were used to identify forearm spots with similar muscle activation patterns.

Results

The best classification of spots with similar activity in simulated ADL consisted in seven muscular-anatomically coherent groups: (1) wrist flexion and ulnar deviation; (2) wrist flexion and radial deviation; (3) digit flexion; (4) thumb extension and abduction/adduction; (5) finger extension; (6) wrist extension and ulnar deviation; and (7) wrist extension and radial deviation.

Conclusion

The number of sEMG sensors could be reduced from 30 to 7 without losing any relevant information, using them as representative spots of the muscular activity of the forearm in simulated ADL. This may help to assess muscle function in rehabilitation while also simplifying the complexity of prosthesis control.

Motor unit drive: a neural interface for real-time upper limb prosthetic control

OBJECTIVE

Modern prosthetic limbs have made strident gains in recent years, incorporating terminal electromechanical devices that are capable of mimicking the human hand. However, access to these advanced control capabilities has been prevented by fundamental limitations of amplitude-based myoelectric neural interfaces, which have remained virtually unchanged for over four decades. Consequently, nearly 23% of adults and 32% of children with major traumatic or congenital upper-limb loss abandon regular use of their myoelectric prosthesis. To address this healthcare need, we have developed a noninvasive neural interface technology that maps natural motor unit increments of neural control and force into biomechanically informed signals for improved prosthetic control.

APPROACH

Our technology, referred to as motor unit drive (MU Drive), utilizes real-time machine learning algorithms for directly measuring motor unit firings from surface electromyographic signals recorded from residual muscles of an amputated or congenitally missing limb. The extracted firings are transformed into biomechanically informed signals based on the force generating properties of individual motor units to provide a control source that represents the intended movement.

MAIN RESULTS

We evaluated the characteristics of the MU Drive control signals and compared them to conventional amplitude-based myoelectric signals in healthy subjects as well as subjects with congenital or traumatic trans-radial limb-loss. Our analysis established a vital proof-of-concept: MU Drive provides a more responsive real-time signal with improved smoothness and more faithful replication of intended limb movement that overcomes the trade-off between performance and latency inherent to amplitude-based myoelectric methods.

SIGNIFICANCE

MU Drive is the first neural interface for prosthetic control that provides noninvasive real-time access to the natural motor control mechanisms of the human nervous system. This new neural interface holds promise for improving prosthetic function by achieving advanced control that better reflects the user intent. Beyond the immediate advantages in the field of prosthetics, MU Drive provides an innovative alternative for advancing the control of exoskeletons, assistive devices, and other robotic rehabilitation applications.

Fascicular nerve stimulation and recording using a novel double-aisle regenerative electrode

OBJECTIVE

As artificial prostheses become more refined, they are most often used as a therapeutic option for hand amputation. By contrast to extra- or intraneural interfaces, regenerative nerve electrodes are designed to enable electrical interfaces with regrowing axonal bundles of injured nerves, aiming to achieve high selectivity for recording and stimulation. However, most of the developed designs pose an obstacle to the regrowth mechanisms due to low transparency and cause impairment to the nerve regeneration.

APPROACH

Here we present the double-aisle electrode, a new type of highly transparent, non-obstructive regenerative electrode. Using a double-side thin-film polyimide planar multi-contact electrode, two nerve fascicles can regenerate without physical impairment through two electrically isolated aisles.

MAIN RESULTS

We show that this electrode can be used to selectively record and stimulate fascicles, acutely as well as chronically, and allow regeneration in nerve gaps of several millimeters without impairment.

SIGNIFICANCE

This multi-aisle regenerative electrode may be suitable for neuroprosthetic applications, such as prostheses, for the restoration of hand function after amputation or severe nerve injuries.

Prostheses as extensions of the body: Progress and challenges

Recent years have seen a surge of interest in the incorporation of artificial limbs. This research promises to provide individuals with sensorimotor disorders such as amputations with prostheses which feel like their own body part. While neuroscience made a leap towards uncovering the basic neurocognitive mechanisms of bodily self-consciousness, the development of incorporated prosthetic limbs still faces substantial challenges in basic neuroscience and in clinical reality. Here we critically examine recent findings on prosthesis incorporation to aid patient rehabilitation in the context of advances in cognitive and applied neuroscience as well as technology. To this end, we integrate results from fundamental and clinical neuropsychological research to outline how several crucial milestones will have to be passed to achieve the self-attribution of prostheses to one's own body. We further discuss the implications of these results for clinical treatment and patients' quality of life.

EMG-Torque Dynamics Change With Contraction Bandwidth

An accurate model for ElectroMyoGram (EMG)-torque dynamics has many uses. One of its applications which has gained high attention among researchers is its use, in estimating the muscle contraction level for the efficient control of prosthesis. In this paper, the dynamic relationship between the surface EMG and torque during isometric contractions at the human ankle was studied using system identification techniques. Subjects voluntarily modulated their ankle torque in dorsiflexion direction, by activating their tibialis anterior muscle, while tracking a pseudo-random binary sequence in a torque matching task. The effects of contraction bandwidth, described by torque spectrum, on EMG-torque dynamics were evaluated by varying the visual command switching time. Nonparametric impulse response functions (IRF) were estimated between the processed surface EMG and torque. It was demonstrated that: 1) at low contraction bandwidths, the identified IRFs had unphysiological anticipatory (i.e., non-causal) components, whose amplitude decreased as the contraction bandwidth increased. We hypothesized that this non-causal behavior arose, because the EMG input contained a component due to feedback from the output torque, i.e., it was recorded from within a closed-loop. Vision was not the feedback source since the non-causal behavior persisted when visual feedback was removed. Repeating the identification using a nonparametric closed-loop identification algorithm yielded causal IRFs at all bandwidths, supporting this hypothesis. 2) EMG-torque dynamics became faster and the bandwidth of system increased as contraction modulation rate increased. Thus, accurate prediction of torque from EMG signals must take into account the contraction bandwidth sensitivity of this system.