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.

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.

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).

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.

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.

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.

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.

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.

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.

Multifaceted understanding of human nerve implants to design optimized electrodes for bioelectronics.

Bioelectronic medicine is a promising venue for treatment of disabilities using implantable neural interfaces. Peripheral neurostimulation of residual nerves recently enabled multiple functional benefits in amputees. Despite the preliminary promising impact on patients' life, the over-time stability of implants and the related nerve reactions are unclear. To unveil the mechanisms and inform the design of better nerve-electrode interfaces, we engaged a multifaceted approach, merging functional responses from patients, their histological data, and corresponding computational modelling. Neurostimulation evoked different selective sensation locations and qualities over-time, with respective perceptual thresholds, that showed different degree of time stabilities dependent from the stimulating active sites. The histological analysis after explant showed mild tissue reactions, while electromechanically active sites and substrates remained conserved. Computational models, based on patients' histology, revealed the direct influence of the simulated tissue reaction to change of thresholds and type of perceived sensations. Novel insights of electrode biocompatibility was observed compared to animals and the increase of thresholds could be predicted computationally. This multifaced framework suggest that future intraneural implants should have easier implantation and higher biocompatibility counteracting the sensations changes through AI-based stimulations and electrode coatings.

Cognitive benefits of using non-invasive compared to implantable neural feedback.

A non-optimal prosthesis integration into an amputee's body schema suggests some important functional and health consequences after lower limb amputation. These include low perception of a prosthesis as a part of the body, experiencing it as heavier than the natural limb, and cognitively exhausting use for users. Invasive approaches, exploiting the surgical implantation of electrodes in residual nerves, improved prosthesis integration by restoring natural and somatotopic sensory feedback in transfemoral amputees. A non-invasive alternative that avoids surgery would reduce costs and shorten certification time, significantly increasing the adoption of such systems. To explore this possibility, we compared results from a non-invasive, electro-cutaneous stimulation system to outcomes observed with the use of implants in above the knee amputees. This non-invasive solution was tested in transfemoral amputees through evaluation of their ability to perceive and recognize touch intensity and locations, or movements of a prosthesis, and its cognitive integration (through dual task performance and perceived prosthesis weight). While this managed to evoke the perception of different locations on the artificial foot, and closures of the leg, it was less performant than invasive solutions. Non-invasive stimulation induced similar improvements in dual motor and cognitive tasks compared to neural feedback. On the other hand, results demonstrate that remapped, evoked sensations are less informative and intuitive than the neural evoked somatotopic sensations. The device therefore fails to improve prosthesis embodiment together with its associated weight perception. This preliminary evaluation meaningfully highlights the drawbacks of non-invasive systems, but also demonstrates benefits when performing multiple tasks at once. Importantly, the improved dual task performance is consistent with invasive devices, taking steps towards the expedited development of a certified device for widespread use.

Optimally-calibrated non-invasive feedback improves amputees' metabolic consumption, balance and walking confidence.

Objective.Lower-limb amputees suffer from a variety of health problems, including higher metabolic consumption and low mobility. These conditions are linked to the lack of a natural sensory feedback (SF) from their prosthetic device, which forces them to adopt compensatory walking strategies that increase fatigue. Recently, both invasive (i.e. requiring a surgery) and non-invasive approaches have been able to provide artificial sensations via neurostimulation, inducing multiple functional and cognitive benefits. Implants helped to improve patient mobility and significantly reduce their metabolic consumption. A wearable, non-invasive alterative that provides similar useful health benefits, would eliminate the surgery related risks and costs thereby increasing the accessibility and the spreading of such neurotechnologies.Approach.Here, we present a non-invasive SF system exploiting an optimally-calibrated (just noticeable difference-based) electro-cutaneous stimulation to encode intensity-modulated foot-ground and knee angle information personalized to the user's just noticeable perceptual threshold. This device was holistically evaluated in three transfemoral amputees by examination of metabolic consumption while walking outdoors, walking over different inclinations on a treadmill indoors, and balance maintenance in reaction to unexpected perturbation on a treadmill indoors. We then collected spatio-temporal parameters (i.e. gait dynamic and kinematics), and self-reported prosthesis confidence while the patients were walking with and without the SF.Main results.This non-invasive SF system, encoding different distinctly perceived levels of tactile and knee flexion information, successfully enabled subjects to decrease metabolic consumption while walking and increase prosthesis confidence. Remarkably, more physiological walking strategies and increased stability in response to external perturbations were observed while walking with the SF.Significance.The health benefits observed with the use of this non-invasive device, previously only observed exploiting invasive technologies, takes an important step towards the development of a practical, non-invasive alternative to restoring SF in leg amputees.

Multisensory stimulation decreases phantom limb distortions and is optimally integrated.

The multisensory integration of signals from different senses is crucial to develop an unambiguous percept of the environment and our body. Losing a limb causes drastic changes in the body, sometimes causing pain and distorted phantom limb perception. Despite the debate over why these phenomena arise, some researchers suggested that they might be linked to an impairment of multisensory signals inflow and integration. Therefore, reestablishing optimally integrated sensory feedback could be crucial. The related benefits on sensory performance and body self-representation are still to be demonstrated, particularly in lower-limb amputees. We present a multisensory framework combining Virtual reality and electro-cutaneous stimulation that allows the optimal integration of visuo-tactile stimuli in lower-limb amputees even if nonspatially matching. We also showed that this multisensory stimulation allowed faster sensory processing, higher embodiment, and reductions in phantom limb distortions. Our findings support the development of multisensory rehabilitation approaches, restoring a correct body representation.

Corrigendum: A Psychometric Platform to Collect Somatosensory Sensations for Neuroprosthetic Use.

[This corrects the article DOI: 10.3389/fmedt.2021.619280.].

A non-invasive wearable sensory leg neuroprosthesis: mechanical, electrical and functional validation.

Objective. Lower limb amputees suffer from a variety of functional deficits related to the absence of sensory communication between the central nervous system and the lost extremity. Indeed, they experience high risk of falls, asymmetric walking and balance, and low prosthesis embodiment, that significantly decrease their quality of life. Presently, there are no commercially available devices able to provide sensory feedback to leg amputees but recently some invasive solutions (i.e. requiring surgery) have been proposed by different research groups. However, a non-invasive effective alternative exploitable in everyday life is still missing.Approach. To address this need we developed and tested a lightweight, non-invasive, wearable technology (NeuroLegs) providing sensory (i.e. knee angle joint and tactile) feedback to the users through electro-cutaneous stimulation. Standard mechanical and electrical tests were performed to assess the safety and reliability of the technology. The NeuroLegs system was verified in terms of accuracy in measuring relevant gait parameters in healthy participants. The effectiveness of the NeuroLegs system at improving walking of three transfemoral amputees was then verified in movement laboratory tests.Main results. No mechanical failures, stable communication among system's parts and a long-lasting battery were demonstrated. A high temporal reliability was found when detecting stride features (important for the real-time configuration) with a correct match to the walking cadence in all assessed walking conditions. Finally, transfemoral amputees showed increased temporal gait symmetry and augmented confidence when walking with the sensory feedback compared to no feedback condition. Stepping outside from the lab, NeuroLegs was successfully exploited by a transfemoral amputee in CYBATHLON Global Edition 2020 in several challenging situations related to daily-living activities.Significance. Our results demonstrate that the NeuroLegs system provides the user with useful sensory information that can be successfully exploited in different walking conditions of daily life.