Development and test of algorithms for the use of peripheral neural interfaces to control robotic systems

In-vivo experiments with neural electrods have been done. Variety of feature extraction and classifcation algorithms have been used in order to choose the optimal one

On the identification of sensory information from mixed nerves by using single-channel cuff electrodes

Background: Several groups have shown that the performance of motor neuroprostheses can be significantly improved by detecting specific sensory events related to the ongoing motor task (e.g., the slippage of an object during grasping). Algorithms have been developed to achieve this goal by processing electroneurographic (ENG) afferent signals recorded by using single-channel cuff electrodes. However, no efforts have been made so far to understand the number and type of detectable sensory events that can be differentiated from whole nerve recordings using this approach. Methods: To this aim, ENG afferent signals, evoked by different sensory stimuli were recorded using single-channel cuff electrodes placed around the sciatic nerve of anesthetized rats. The ENG signals were digitally processed and several features were extracted and used as inputs for the classification. The work was performed on integral datasets, without eliminating any noisy parts, in order to be as close as possible to real application. Results: The results obtained showed that single-channel cuff electrodes are able to provide information on two to three different afferent (proprioceptive, mechanical and nociceptive) stimuli, with reasonably good discrimination ability. The classification performances are affected by the SNR of the signal, which in turn is related to the diameter of the fibers encoding a particular type of neurophysiological stimulus. Conclusions: Our findings indicate that signals of acceptable SNR and corresponding to different physiological modalities (e.g. mediated by different types of nerve fibers) may be distinguished

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

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

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting

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

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

Advancing limb neural prostheses

ISSN:0036-8075ISSN:1095-920

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.ISSN:1743-000