Stanisa Raspopovic

Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses

A multigrasp, bidirectional hand prosthesis delivers dynamic sensory feedback, allowing a user with a hand amputation to achieve fine grasping force control and realistic object sensing. An Artificial Hand’s Sense of Touch To feel the hard curvature of a baseball or the soft cylinder that is a soda can—these sensations we often take for granted. But amputees with a prosthetic arm know only that they are holding an object, the shape and stiffness discernible only by eye or from experience. Toward a more sophisticated prosthetic that can “feel” an object, Raspopovic and colleagues incorporated a feedback system connected to the amputee’s arm nerves, which delivers sensory information in real time. The authors connected electrodes in the arm nerves to sensors in two fingers of the prosthetic hand. To “feel” an object, the electrodes delivered electrical stimuli to the nerves that were proportional to the finger sensor readouts. To grasp an object and perform other motor commands, muscle signals were decoded. This bidirectional hand prosthetic was tested in a single amputee who was blindfolded and acoustically shielded to assure that sound and vision were not being used to manipulate objects. In more than 700 trials, the subject showed that he could modulate force and grasp and identify physical characteristics of different types of objects, such as cotton balls, an orange, and a piece of wood. Such sensory feedback with precise control over a hand prosthetic would allow amputees to more freely and naturally explore their environments. Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user’s intentions and the delivery of nearly “natural” sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and “life-like” quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.

Double nerve intraneural interface implant on a human amputee for robotic hand control

OBJECTIVES The principle underlying this project is that, despite nervous reorganization following upper limb amputation, original pathways and CNS relays partially maintain their function and can be exploited for interfacing prostheses. Aim of this study is to evaluate a novel peripheral intraneural multielectrode for multi-movement prosthesis control and for sensory feed-back, while assessing cortical reorganization following the re-acquired stream of data. METHODS Four intrafascicular longitudinal flexible multielectrodes (tf-LIFE4) were implanted in the median and ulnar nerves of an amputee; they reliably recorded output signals for 4 weeks. Artificial intelligence classifiers were used off-line to analyse LIFE signals recorded during three distinct hand movements under voluntary order. RESULTS Real-time control of motor output was achieved for the three actions. When applied off-line artificial intelligence reached >85% real-time correct classification of trials. Moreover, different types of current stimulation were determined to allow reproducible and localized hand/fingers sensations. Cortical organization was observed via TMS in parallel with partial resolution of symptoms due to the phantom-limb syndrome (PLS). CONCLUSIONS tf-LIFE4s recorded output signals in human nerves for 4 weeks, though the efficacy of sensory stimulation decayed after 10 days. Recording from a number of fibres permitted a high percentage of distinct actions to be classified correctly. Reversal of plastic changes and alleviation of PLS represent corollary findings of potential therapeutic benefit. SIGNIFICANCE This study represents a breakthrough in robotic hand use in amputees.

Control of Hand Prostheses Using Peripheral Information

Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. Choosing which voluntary signal to use for control purposes is a critical element to achieve this goal. This review presents and discusses the recent results achieved by using electromyographic signals, recorded either with surface (sEMG) or intramuscular (iEMG) electrodes, and electroneurographic (ENG) signals. The potential benefits and shortcomings of the different approaches are described with a particular attention to the definition of all the steps required to achieve an effective hand prosthesis control in the different cases. Finally, a possible roadmap in the field is also presented.

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.

Closed-loop neuromodulation of spinal sensorimotor circuits controls refined locomotion after complete spinal cord injury.

Closed-loop neuromodulation of spinal sensorimotor circuits allows high-fidelity control over leg movements in paralyzed rats. Closing the Loop on Neuroprosthetic Control Patients with spinal cord injury (SCI) and paralysis may soon be outfitted with so-called neuromodulation devices, which electrically stimulate the brain or spinal cord, causing movement in the legs. Currently, tuning such modulation requires constant observation and patient-specific adjustments, which are not ideal for fluid movement or for broad translation of these technologies to injured patients. In response, Wenger et al. have created a closed-loop system that will essentially “auto-tune” the device, allowing the paralyzed patient—or, in their study, the paralyzed rat—to move freely, without worrying about adjusting electrical pulse width, amplitude, or frequency. The authors gave rats complete SCI epidural electrical stimulation and then mapped their leg movements and sensorimotor responses while in a body support system, walking upright (bipedal) on a treadmill, or climbing stairs. From this information, they devised a computational system that integrated feedback and feed-forward models for closed-loop, continuous control of leg movement and, in turn, a more natural locomotion. Closed-loop neuromodulation of spinal circuits could impart fluid motor control and prevent fatigue when rehabilitating patients with SCI. Neuromodulation of spinal sensorimotor circuits improves motor control in animal models and humans with spinal cord injury. With common neuromodulation devices, electrical stimulation parameters are tuned manually and remain constant during movement. We developed a mechanistic framework to optimize neuromodulation in real time to achieve high-fidelity control of leg kinematics during locomotion in rats. We first uncovered relationships between neuromodulation parameters and recruitment of distinct sensorimotor circuits, resulting in predictive adjustments of leg kinematics. Second, we established a technological platform with embedded control policies that integrated robust movement feedback and feed-forward control loops in real time. These developments allowed us to conceive a neuroprosthetic system that controlled a broad range of foot trajectories during continuous locomotion in paralyzed rats. Animals with complete spinal cord injury performed more than 1000 successive steps without failure, and were able to climb staircases of various heights and lengths with precision and fluidity. Beyond therapeutic potential, these findings provide a conceptual and technical framework to personalize neuromodulation treatments for other neurological disorders.

Decoding Information From Neural Signals Recorded Using Intraneural Electrodes: Toward the Development of a Neurocontrolled Hand Prosthesis

The possibility of controlling dexterous hand prostheses by using a direct connection with the nervous system is particularly interesting for the significant improvement of the quality of life of patients, which can derive from this achievement. Among the various approaches, peripheral nerve based intrafascicular electrodes are excellent neural interface candidates, representing an excellent compromise between high selectivity and relatively low invasiveness. Moreover, this approach has undergone preliminary testing in human volunteers and has shown promise. In this paper, we investigate whether the use of intrafascicular electrodes can be used to decode multiple sensory and motor information channels with the aim to develop a finite state algorithm that may be employed to control neuroprostheses and neurocontrolled hand prostheses. The results achieved both in animal and human experiments show that the combination of multiple sites recordings and advanced signal processing techniques (such as wavelet denoising and spike sorting algorithms) can be used to identify both sensory stimuli (in animal models) and motor commands (in a human volunteer). These findings have interesting implications, which should be investigated in future experiments.

Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans

Restoration of touch after hand amputation is a desirable feature of ideal prostheses. Here, we show that texture discrimination can be artificially provided in human subjects by implementing a neuromorphic real-time mechano-neuro-transduction (MNT), which emulates to some extent the firing dynamics of SA1 cutaneous afferents. The MNT process was used to modulate the temporal pattern of electrical spikes delivered to the human median nerve via percutaneous microstimulation in four intact subjects and via implanted intrafascicular stimulation in one transradial amputee. Both approaches allowed the subjects to reliably discriminate spatial coarseness of surfaces as confirmed also by a hybrid neural model of the median nerve. Moreover, MNT-evoked EEG activity showed physiologically plausible responses that were superimposable in time and topography to the ones elicited by a natural mechanical tactile stimulation. These findings can open up novel opportunities for sensory restoration in the next generation of neuro-prosthetic hands. DOI: http://dx.doi.org/10.7554/eLife.09148.001

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

BackgroundThe 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 users 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.MethodsThin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputees stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm.ResultsThe results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm.ConclusionsThese results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

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

BackgroundSeveral 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.MethodsTo 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.ResultsThe 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.ConclusionsOur 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.

A Computational Model for the Stimulation of Rat Sciatic Nerve Using a Transverse Intrafascicular Multichannel Electrode

Neuroprostheses based on electrical stimulation could potentially help disabled persons. They are based on neural interface that aim at creating an intimate contact with neural cells. The efficacy of neuroprostheses can be improved by increasing the selectivity of the neural interfaces used to stimulate specific subsets of cells. Selectivity is strongly influenced by interface design. Computer models can be useful for exploring the high dimensional space of design parameters with the aim to provide guidelines for the development of more efficient electrodes, with minimal animal use and optimization of manufacturing processes. The purpose of this study was to implement a realistic model of the performance of a transverse intrafascicular multichannel electrode (TIME) implanted into the rat sciatic nerve. A realistic finite element method (FEM) model was developed taking into account the anatomical and physiological features of the rat sciatic nerve. Electric potentials were calculated and interpolated voltages were applied to the model of a rat sciatic nerve axon, based on experimental biophysical data. Results indicate that high intrafascicular and inter-fascicular selectivity values with low current levels can be achieved with TIMEs. The selectivity of TIMEs was also compared to an extraneural electrode, showing that higher selectivity with less current can be obtained. Using this model, the robustness of electrode performances for translational and rotational displacements were evaluated.