Design of an adaptable intrafascicular electrode (AIR) for selective nerve stimulation by model-based optimization.

Peripheral nerve stimulation is being investigated as a therapeutic tool in several clinical scenarios. However, the adopted devices have restricted ability to obtain desired outcomes with tolerable off-target effects. Recent promising solutions are not yet employed in clinical practice due to complex required surgeries, lack of long-term stability, and implant invasiveness. Here, we aimed to design a neural interface to address these issues, specifically dimensioned for pudendal and sacral nerves to potentially target sexual, bladder, or bowel dysfunctions. We designed the adaptable intrafascicular radial electrode (AIR) through realistic computational models. They account for detailed human anatomy, inhomogeneous anisotropic conductance, following the trajectories of axons along curving and branching fascicles, and detailed biophysics of axons. The model was validated against available experimental data. Thanks to computationally efficient geometry-based selectivity estimations we informed the electrode design, optimizing its dimensions to obtain the highest selectivity while maintaining low invasiveness. We then compared the AIR with state-of-the-art electrodes, namely InterStim leads, multipolar cuffs and transversal intrafascicular multichannel electrodes (TIME). AIR, comprising a flexible substrate, surface active sites, and radially inserted intrafascicular needles, is designed to be implanted in a few standard steps, potentially enabling fast implants. It holds potential for repeatable stimulation outcomes thanks to its radial structural symmetry. When compared in-silico, AIR consistently outperformed cuff electrodes and InterStim leads in terms of recruitment threshold and stimulation selectivity. AIR performed similarly or better than a TIME, with quantified less invasiveness. Finally, we showed how AIR can adapt to different nerve sizes and varying shapes while maintaining high selectivity. The AIR electrode shows the potential to fill a clinical need for an effective peripheral nerve interface. Its high predicted performance in all the identified requirements was enabled by a model-based approach, readily applicable for the optimization of electrode parameters in any peripheral nerve stimulation scenario.

Automated calibration of somatosensory stimulation using reinforcement learning.

BACKGROUND: The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses. METHODS: We developed an RL algorithm to automatically select neurostimulation parameters for restoring sensory feedback with transcutaneous electrical nerve stimulation (TENS). First, the algorithm was trained offline on a dataset comprising 49 subjects. Then, the neurostimulation was then integrated with a graphical user interface (GUI) to create an intuitive AI-based mapping platform enabling the user to autonomously perform the sensation characterization procedure. We assessed the algorithm against the performance of both experienced and naïve and of a brute force algorithm (BFA), on 15 nerves from five subjects. Then, we validated the AI-based platform on six neuropathic nerves affected by distal sensory loss. RESULTS: Our automatized approach demonstrated the ability to find the optimal values of neurostimulation achieving reliable and comfortable elicited sensations. When compared to alternatives, RL outperformed the naïve and BFA, significantly decreasing the time for mapping and the number of delivered stimulation trains, while improving the overall quality. Furthermore, the RL algorithm showed performance comparable to trained experimenters. Finally, we exploited it successfully for eliciting sensory feedback in neuropathic patients. CONCLUSIONS: Our findings demonstrated that the AI-based platform based on a RL algorithm can automatically and efficiently calibrate parameters for somatosensory nerve stimulation. This holds promise to avoid experts' employment in similar scenarios, thanks to the merging between AI and neurotech. Our RL algorithm has the potential to be used in other neuromodulation fields requiring a mapping process of the stimulation parameters. TRIAL REGISTRATION: ClinicalTrial.gov (Identifier: NCT04217005).

Sensory feedback for limb prostheses in amputees.

Commercial prosthetic devices currently do not provide natural sensory information on the interaction with objects or movements. The subsequent disadvantages include unphysiological walking with a prosthetic leg and difficulty in controlling the force exerted with a prosthetic hand, thus creating health issues. Restoring natural sensory feedback from the prosthesis to amputees is an unmet clinical need. An optimal device should be able to elicit natural sensations of touch or proprioception, by delivering the complex signals to the nervous system that would be produced by skin, muscles and joints receptors. This Review covers the various neurotechnological approaches that have been proposed for the development of the optimal sensory feedback restoration device for arm and leg amputees.

Six-Month Assessment of a Hand Prosthesis with Intraneural Tactile Feedback.

OBJECTIVE: Hand amputation is a highly disabling event, which significantly affects quality of life. An effective hand replacement can be achieved if the user, in addition to motor functions, is provided with the sensations that are naturally perceived while grasping and moving. Intraneural peripheral electrodes have shown promising results toward the restoration of the sense of touch. However, the long-term usability and clinical relevance of intraneural sensory feedback have not yet been clearly demonstrated. METHODS: To this aim, we performed a 6-month clinical study with 3 transradial amputees who received implants of transverse intrafascicular multichannel electrodes (TIMEs) in their median and ulnar nerves. After calibration, electrical stimulation was delivered through the TIMEs connected to artificial sensors in the digits of a prosthesis to generate sensory feedback, which was then used by the subjects while performing different grasping tasks. RESULTS: All subjects, notwithstanding their important clinical differences, reported stimulation-induced sensations from the phantom hand for the whole duration of the trial. They also successfully integrated the sensory feedback into their motor control strategies while performing experimental tests simulating tasks of real life (with and without the support of vision). Finally, they reported a decrement of their phantom limb pain and a general improvement in mood state. INTERPRETATION: The promising results achieved with all subjects show the feasibility of the use of intraneural stimulation in clinical settings. ANN NEUROL 2019;85:137-154.

A computational model to design neural interfaces for lower-limb sensory neuroprostheses.

BACKGROUND: Leg amputees suffer the lack of sensory feedback from a prosthesis, which is connected to their low confidence during walking, falls and low mobility. Electrical peripheral nerve stimulation (ePNS) of upper-limb amputee's residual nerves has shown the ability to restore the sensations from the missing limb via intraneural (TIME) and epineural (FINE) neural interfaces. Physiologically plausible stimulation protocols targeting lower limb sciatic nerve hold promise to induce sensory feedback restoration that should facilitate close-to-natural sensorimotor integration and therefore walking corrections. The sciatic nerve, innervating the foot and lower leg, has very different dimensions in respect to upper-limb nerves. Therefore, there is a need to develop a computational model of its behavior in response to the ePNS. METHODS: We employed a hybrid FEM-NEURON model framework for the development of anatomically correct sciatic nerve model. Based on histological images of two distinct sciatic nerve cross-sections, we reconstructed accurate FEM models for testing neural interfaces. Two different electrode types (based on TIME and FINE) with multiple active sites configurations were tested and evaluated for efficiency (selective recruitment of fascicles). We also investigated different policies of stimulation (monopolar and bipolar), as well as the optimal number of implants. Additionally, we optimized the existing simulation framework significantly reducing the computational load. RESULTS: The main findings achieved through our modelling study include electrode manufacturing and surgical placement indications, together with beneficial stimulation policy of use. It results that TIME electrodes with 20 active sites are optimal for lower limb and the same number has been obtained for FINE electrodes. To interface the huge sciatic nerve, model indicates that 3 TIMEs is the optimal number of surgically implanted electrodes. Through the bipolar policy of stimulation, all studied configurations were gaining in the efficiency. Also, an indication for the optimized computation is given, which decreased the computation time by 80%. CONCLUSIONS: This computational model suggests the optimal interfaces to use in human subjects with lower limb amputation, their surgical placement and beneficial bipolar policy of stimulation. It will potentially enable the clinical translation of the sensory neuroprosthetics towards the lower limb applications.

Sensitivity to temporal parameters of intraneural tactile sensory feedback.

BACKGROUND: Recent studies have shown that neural stimulation can be used to provide artificial sensory feedback to amputees eliciting sensations referred on the amputated hand. The temporal properties of the neural stimulation modulate aspects of evoked sensations that can be exploited in a bidirectional hand prosthesis. METHODS: We previously collected evidence that the derivative of the amplitude of the stimulation (intra-digit temporal dynamics) allows subjects to recognize object compliance and that the time delay among stimuli injected through electrodes implanted in different nerves (inter-digit temporal distance) allows to recognize object shapes. Nevertheless, a detailed characterization of the subjects' sensitivity to variations of intra-digit temporal dynamic and inter-digit temporal distance of the intraneural tactile feedback has not been executed. An exhaustive understanding of the overall potentials and limits of intraneural stimulation to deliver sensory feedback is of paramount importance to bring this approach closer and closer to the natural situation. To this aim, here we asked two trans-radial amputees to identify stimuli with different temporal characteristics delivered to the same active site (intra-digit temporal Dynamic Recognition (DR)) or between two active sites (inter-digit Temporal distance Recognition (TR)). Finally, we compared the results achieved for (simulated) TR with conceptually similar experiments with real objects with one subject. RESULTS: We found that the subjects were able to identify stimuli with temporal differences (perceptual thresholds) larger than 0.25 s for DR and larger than 0.125 s for TR, respectively. Moreover, we also found no statistically significant differences when the subjects were asked to identify three objects during simulated 'open-loop' TR experiments or real 'closed-loop' tests while controlling robotic hand. CONCLUSIONS: This study is a new step towards a more detailed analysis of the overall potentials and limits of intraneural sensory feedback. A full characterization is necessary to develop more advanced prostheses capable of restoring all lost functions and of being perceived more as a natural limb by users.

Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses.

BACKGROUND: The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfortunately, this choice is still restrained by the reduced knowledge of the dynamics of human efferent signals recorded from the nerves and associated to hand movements. FINDINGS: To address this issue, in this work we acquired neural efferent activities from healthy subjects performing hand-related tasks using ultrasound-guided microneurography, a minimally invasive technique, which employs needles, inserted percutaneously, to record from nerve fibers. These signals allowed us to identify neural features correlated with force and velocity of finger movements that were used to decode motor intentions. We developed computational models, which confirmed the potential translatability of these results showing how these neural features hold in absence of feedback and when implantable intrafascicular recording, rather than microneurography, is performed. CONCLUSIONS: Our results are a proof of principle that microneurography could be used as a useful tool to assist the development of more effective hand prostheses.

An integrated interface for peripheral neural system recording and stimulation: system design, electrical tests and in-vivo results.

The prototype of an electronic bi-directional interface between the Peripheral Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is composed of 2 integrated circuits: a standard CMOS device for neural recording and a HVCMOS device for neural stimulation. The integrated circuits have been realized in 2 different 0.35µ m CMOS processes available from ams. The complete system incorporates 8 channels each including the analog front-end, the A/D conversion, based on a sigma delta architecture and a programmable stimulation module implemented as a 5-bit current DAC; two voltage boosters supply the output stimulation stage with a programmable voltage scalable up to 17V. Successful in-vivo experiments with rats having a TIME electrode implanted in the sciatic nerve were carried out, showing the capability of recording neural signals in the tens of microvolts, with a global noise of 7µ V r m s , and to selectively elicit the tibial and plantar muscles using different active sites of the electrode.

A computational model for epidural electrical stimulation of spinal sensorimotor circuits.

Epidural electrical stimulation (EES) of lumbosacral segments can restore a range of movements after spinal cord injury. However, the mechanisms and neural structures through which EES facilitates movement execution remain unclear. Here, we designed a computational model and performed in vivo experiments to investigate the type of fibers, neurons, and circuits recruited in response to EES. We first developed a realistic finite element computer model of rat lumbosacral segments to identify the currents generated by EES. To evaluate the impact of these currents on sensorimotor circuits, we coupled this model with an anatomically realistic axon-cable model of motoneurons, interneurons, and myelinated afferent fibers for antagonistic ankle muscles. Comparisons between computer simulations and experiments revealed the ability of the model to predict EES-evoked motor responses over multiple intensities and locations. Analysis of the recruited neural structures revealed the lack of direct influence of EES on motoneurons and interneurons. Simulations and pharmacological experiments demonstrated that EES engages spinal circuits trans-synaptically through the recruitment of myelinated afferent fibers. The model also predicted the capacity of spatially distinct EES to modulate side-specific limb movements and, to a lesser extent, extension versus flexion. These predictions were confirmed during standing and walking enabled by EES in spinal rats. These combined results provide a mechanistic framework for the design of spinal neuroprosthetic systems to improve standing and walking after neurological disorders.

Bionic blink improves real-time eye closure in unilateral facial paralysis.

Facial paralysis is the inability to move facial muscles thereby impairing the ability to blink and make facial expressions. Depending on the localization of the nerve malfunction it is subcategorised into central or peripheral and is usually unilateral. This leads to health deficits stemming from corneal dryness and social ostracization.Objective: Electrical stimulation shows promise as a method through which to restore the blink function and as a result improve eye health. However, it is unknown whether a real-time, myoelectrically controlled, neurostimulating device can be used as assistance to this pathological condition.Approach: We developed NEURO-BLINK, a wearable robotic system, that can detect the volitional healthy contralateral blink through electromyography and electrically stimulate the impaired subcutaneous facial nerve and orbicularis oculi muscle to compensate for lost blink function. Alongside the system, we developed a method to evaluate optimal electrode placement through the relationship between blink amplitude and injected charge.Main results: Ten patients with unilateral facial palsy were enrolled in the NEURO-BLINK study, with eight completing testing under two conditions. (1) where the stimulation was cued with an auditory signal (i.e. paced controlled) and (2) synchronized with the natural blink (i.e. myoelectrically controlled). In both scenarios, overall eye closure (distance between eyelids) and cornea coverage measured with high FPS video were found to significantly improve when measured in real-time, while no significant clinical changes were found immediately after use.Significance: This work takes steps towards the development of a portable medical device for blink restoration and facial stimulation which has the potential to improve long-term ocular health.

Biomimetic computer-to-brain communication enhancing naturalistic touch sensations via peripheral nerve stimulation.

Artificial communication with the brain through peripheral nerve stimulation shows promising results in individuals with sensorimotor deficits. However, these efforts lack an intuitive and natural sensory experience. In this study, we design and test a biomimetic neurostimulation framework inspired by nature, capable of "writing" physiologically plausible information back into the peripheral nervous system. Starting from an in-silico model of mechanoreceptors, we develop biomimetic stimulation policies. We then experimentally assess them alongside mechanical touch and common linear neuromodulations. Neural responses resulting from biomimetic neuromodulation are consistently transmitted towards dorsal root ganglion and spinal cord of cats, and their spatio-temporal neural dynamics resemble those naturally induced. We implement these paradigms within the bionic device and test it with patients (ClinicalTrials.gov identifier NCT03350061). He we report that biomimetic neurostimulation improves mobility (primary outcome) and reduces mental effort (secondary outcome) compared to traditional approaches. The outcomes of this neuroscience-driven technology, inspired by the human body, may serve as a model for advancing assistive neurotechnologies.

Neural population dynamics reveals disruption of spinal circuits' responses to proprioceptive input during electrical stimulation of sensory afferents.

While neurostimulation technologies are rapidly approaching clinical applications for sensorimotor disorders, the impact of electrical stimulation on network dynamics is still unknown. Given the high degree of shared processing in neural structures, it is critical to understand if neurostimulation affects functions that are related to, but not targeted by, the intervention. Here, we approach this question by studying the effects of electrical stimulation of cutaneous afferents on unrelated processing of proprioceptive inputs. We recorded intraspinal neural activity in four monkeys while generating proprioceptive inputs from the radial nerve. We then applied continuous stimulation to the radial nerve cutaneous branch and quantified the impact of the stimulation on spinal processing of proprioceptive inputs via neural population dynamics. Proprioceptive pulses consistently produce neural trajectories that are disrupted by concurrent cutaneous stimulation. This disruption propagates to the somatosensory cortex, suggesting that electrical stimulation can perturb natural information processing across the neural axis.

Recalibration of neuromodulation parameters in neural implants with adaptive Bayesian optimization.

Objective. Neuromodulation technology holds promise for treating conditions where physiological mechanisms of neural activity have been affected. To make treatments efficient and devices highly effective, neurostimulation protocols must be personalized. The interface between the targeted nervous tissue and the neurotechnology (i.e. human-machine link or neural interface) usually requires constant re-calibration of neuromodulation parameters, due to many different biological and microscale phenomena happening over-time. This adaptation of the optimal stimulation parameters generally involves an expert-mediated re-calibration, with corresponding economic burden, compromised every-day usability and efficacy of the device, and consequent loss of time and increased discomfort of patients going back to clinics to get the device tuned. We aim to construct an adaptable AI-based system, able to compensate for these changes autonomously.Approach. We exploited Gaussian process-based Bayesian optimization (GPBO) methods to re-adjust the neurostimulation parameters in realistic neuroprosthetic data by integrating temporal information into the process to tackle the issue of time variability. To this aim, we built a predictive model able to tune the neuromodulation parameters in two separate crucial scenarios where re-calibration is needed. In the first one, we built a model able to find the optimal active sites in a multichannel electrode, i.e. able to cover a certain function for a neuroprosthesis, which in this specific case was the evoked-sensation location variability. In the second one, we propose an algorithm able to adapt the injected charge required to obtain a functional neural activation (e.g. perceptual threshold variability). By retrospectively collecting the outcomes from the calibration experiments in a human clinical trial utilizing implantable neuromodulation devices, we were able to quantitatively assess our GPBO-based approach in an offline setting.Main results.Our automatic algorithm can successfully adapt neurostimulation parameters to evoked-sensation location changes and to perceptual threshold changes over-time. These findings propose a quick, automatic way to tackle the inevitable variability of neurostimulation parameters over time. Upon validation in other frameworks it increases the usability of this technology through decreasing the time and the cost of the treatment supporting the potential for future widespread use. This work suggests the exploitation of AI-based methods for developing the next generation of 'smart' neuromodulation devices.

Brain-Computer Interface to Deliver Individualized Multisensory Intervention for Neuropathic Pain.

To unravel the complexity of the neuropathic pain experience, researchers have tried to identify reliable pain signatures (biomarkers) using electroencephalography (EEG) and skin conductance (SC). Nevertheless, their use as a clinical aid to design personalized therapies remains scarce and patients are prescribed with common and inefficient painkillers. To address this need, novel non-pharmacological interventions, such as transcutaneous electrical nerve stimulation (TENS) to activate peripheral pain relief via neuromodulation and virtual reality (VR) to modulate patients' attention, have emerged. However, all present treatments suffer from the inherent bias of the patient's self-reported pain intensity, depending on their predisposition and tolerance, together with unspecific, pre-defined scheduling of sessions which does not consider the timing of pain episodes onset. Here, we show a Brain-Computer Interface (BCI) detecting in real-time neurophysiological signatures of neuropathic pain from EEG combined with SC and accordingly triggering a multisensory intervention combining TENS and VR. After validating that the multisensory intervention effectively decreased experimentally induced pain, the BCI was tested with thirteen healthy subjects by electrically inducing pain and showed 82% recall in decoding pain in real time. Such constructed BCI was then validated with eight neuropathic patients reaching 75% online pain precision, and consequently releasing the intervention inducing a significant decrease (50% NPSI score) in neuropathic patients' pain perception. Our results demonstrate the feasibility of real-time pain detection from objective neurophysiological signals, and the effectiveness of a triggered combination of VR and TENS to decrease neuropathic pain. This paves the way towards personalized, data-driven pain therapies using fully portable technologies.

Symbiotic electroneural and musculoskeletal framework to encode proprioception via neurostimulation: ProprioStim.

Peripheral nerve stimulation in amputees achieved the restoration of touch, but not proprioception, which is critical in locomotion. A plausible reason is the lack of means to artificially replicate the complex activity of proprioceptors. To uncover this, we coupled neuromuscular models from ten subjects and nerve histologies from two implanted amputees to develop ProprioStim: a framework to encode proprioception by electrical evoking neural activity in close agreement with natural proprioceptive activity. We demonstrated its feasibility through non-invasive stimulation on seven healthy subjects comparing it with standard linear charge encoding. Results showed that ProprioStim multichannel stimulation was felt more natural, and hold promises for increasing accuracy in knee angle tracking, especially in future implantable solutions. Additionally, we quantified the importance of realistic 3D-nerve models against extruded models previously adopted for further design and validation of novel neurostimulation encoding strategies. ProprioStim provides clear guidelines for the development of neurostimulation policies restoring natural proprioception.

Reshaping the full body illusion through visuo-electro-tactile sensations.

The physical boundaries of our body do not define what we perceive as self. This malleable representation arises from the neural integration of sensory information coming from the environment. Manipulating the visual and haptic cues produces changes in body perception, inducing the Full Body Illusion (FBI), a vastly used approach to exploring humans' perception. After pioneering FBI demonstrations, issues arose regarding its setup, using experimenter-based touch and pre-recorded videos. Moreover, its outcome measures are based mainly on subjective reports, leading to biased results, or on heterogeneous objective ones giving poor consensus on their validity. To address these limitations, we developed and tested a multisensory platform allowing highly controlled experimental conditions, thanks to the leveraged use of innovative technologies: Virtual Reality (VR) and Transcutaneous Electrical Nerve Stimulation (TENS). This enabled a high spatial and temporal precision of the visual and haptic cues, efficiently eliciting FBI. While it matched the classic approach in subjective measures, our setup resulted also in significant results for all objective measurements. Importantly, FBI was elicited when all 4 limbs were multimodally stimulated but also in a single limb condition. Our results behoove the adoption of a comprehensive set of measures, introducing a new neuroscientific platform to investigate body representations.

Modeling foot sole cutaneous afferents: FootSim.

While walking and maintaining balance, humans rely on cutaneous feedback from the foot sole. Electrophysiological recordings reveal how this tactile feedback is represented in neural afferent populations, but obtaining them is difficult and limited to stationary conditions. We developed the FootSim model, a realistic replication of mechanoreceptor activation in the lower limb. The model simulates neural spiking responses to arbitrary mechanical stimuli from the combined population of all four types of mechanoreceptors innervating the foot sole. It considers specific mechanics of the foot sole skin tissue, and model internal parameters are fitted using human microneurography recording dataset. FootSim can be exploited for neuroscientific insights, to understand the overall afferent activation in dynamic conditions, and for overcoming the limitation of currently available recording techniques. Furthermore, neuroengineers can use the model as a robust in silico tool for neuroprosthetic applications and for designing biomimetic stimulation patterns starting from the simulated afferent neural responses.

Multiparametric non-linear TENS modulation to integrate intuitive sensory feedback.

Objective. Transcutaneous electrical nerve stimulation (TENS) has been recently introduced in neurorehabilitation and neuroprosthetics as a promising, non-invasive sensory feedback restoration alternative to implantable neurostimulation. Yet, the adopted stimulation paradigms are typically based on single-parameter modulations (e.g. pulse amplitude (PA), pulse-width (PW) or pulse frequency (PF)). They elicit artificial sensations characterized by a low intensity resolution (e.g. few perceived levels), low naturalness and intuitiveness, hindering the acceptance of this technology. To address these issues, we designed novel multiparametric stimulation paradigms, featuring the simultaneous modulation of multiple parameters, and implemented them in real-time tests of performance when exploited as artificial sensory inputs.Approach. We initially investigated the contribution of PW and PF variations to the perceived sensation magnitude through discrimination tests. Then, we designed three multiparametric stimulation paradigms comparing them with a standard PW linear modulation in terms of evoked sensation naturalness and intensity. The most performant paradigms were then implemented in real-time in a Virtual Reality-TENS platform to assess their ability to provide intuitive somatosensory feedback in a functional task.Main results. Our study highlighted a strong negative correlation between perceived naturalness and intensity: less intense sensations are usually deemed as more similar to natural touch. In addition, we observed that PF and PW changes have a different weight on the perceived sensation intensity. As a result, we adapted the activation charge rate (ACR) equation, proposed for implantable neurostimulation to predict the perceived intensity while co-modulating the PF and charge per pulse, to TENS (ACRT). ACRTallowed to design different multiparametric TENS paradigms with the same absolute perceived intensity. Although not reported as more natural, the multiparametric paradigm, based on sinusoidal PF modulation, resulted being more intuitive and subconsciously integrated than the standard linear one. This allowed subjects to achieve a faster and more accurate functional performance.Significance. Our findings suggest that TENS-based, multiparametric neurostimulation, despite not consciously perceived naturally, can provide integrated and more intuitive somatosensory information, as functionally proved. This could be exploited to design novel encoding strategies able to improve the performance of non-invasive sensory feedback technologies.

Sensory-Motor Neurostimulation to Enhance Exosuit Performance.

Exosuits typically provide limited mechanical support and rely on a user's residual functional ability. However, people with neurological impairments often suffer from both motor and sensory deficits that limit the assistance an exosuit can provide. To overcome these limitations, we developed the REINFORCE system, that complements the mechanical assistance provided by an exosuit, the Myosuit, with (1) functional electrical stimulation to enhance the activities of leg muscles, and (2) transcutaneous electrical nerve stimulation to restore somatosensory information. It consists of a fully portable and highly modular system that can be easily adapted to the level of impairment and specific need of each participant. Technical verification with three healthy participants showed reliable synchronization between all modules of the systems in all phases of walking. Additionally, we tested the system's effectiveness in one participant with multiple sclerosis who walked overground with and without functional electrical stimulation. Results showed a slight increase in self-selected walking speed (approx. 18%) and in the peak hip flexion at late swing (approx. 12%) as well as reduced step-to-step variability of step length and step time when electrical stimulation was provided. Our findings push towards a clinical trial involving more patients to validate the effectiveness of the REINFORCE system on participants' mobility.