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Objectives We provide an appraisal of recent research on stimulation of the auditory system with light. In particular, we discuss direct infrared stimulation and ongoing controversies regarding the feasibility of this modality. We also discuss advancements and barriers to the development of an optical cochlear implant. Methods This is a review article that covers relevant animal studies. Results The auditory system has been stimulated with infrared light, and in a much more spatially selective manner than with electrical stimulation. However, there are experiments from other labs that have not been able to reproduce these results. This has resulted in an ongoing controversy regarding the feasibility of infrared stimulation, and the reasons for these experimental differences still require explanation. The neural response characteristics also appear to be much different than with electrical stimulation. The electrical stimulation paradigms used for modern cochlear implants do not apply well to optical stimulation and new coding strategies are under development. Stimulation with infrared light brings the risk of heat accumulation in the tissue at high pulse repetition rates, so optimal pulse shapes and combined optical/electrical stimulation are being investigated to mitigate this. Optogenetics is another promising technique, which makes neurons more sensitive to light stimulation by inserting light sensitive ion channels via viral vectors. Challenges of optogenetics include the expression of light sensitive channels in sufficient density in the target neurons, and the risk of damaging neurons by the expression of a foreign protein. Conclusion Optical stimulation of the nervous system is a promising new field, and there has been progress toward the development of a cochlear implant that takes advantage of the benefits of optical stimulation. There are barriers, and controversies, but so far none that seem intractable. Level of evidence NA (animal studies and basic research)., We provide an update on the development of near‐infrared stimulation of the auditory system over the past decade. In particular, we discuss the use of infrared stimulation and progress towards an optical cochlear implant.

Background Electrical stimulation of residual afferent nerve fibers can evoke sensations from a missing limb after amputation, and bionic arms endowed with artificial sensory feedback have been shown to confer functional and psychological benefits. Here we explore the extent to which artificial sensations can be discriminated based on location, quality, and intensity. Methods We implanted Utah Slanted Electrode Arrays (USEAs) in the arm nerves of three transradial amputees and delivered electrical stimulation via different electrodes and frequencies to produce sensations on the missing hand with various locations, qualities, and intensities. Participants performed blind discrimination trials to discriminate among these artificial sensations. Results Participants successfully discriminated cutaneous and proprioceptive sensations ranging in location, quality and intensity. Performance was significantly greater than chance for all discrimination tasks, including discrimination among up to ten different cutaneous location-intensity combinations (15/30 successes, p p p p p Conclusion Electrode location, stimulation frequency, and stimulation pattern can be modulated to evoke functionally discriminable sensations with a range of locations, qualities, and intensities. This rich source of artificial sensory feedback may enhance functional performance and embodiment of bionic arms endowed with a sense of touch.

Restoring vision by stimulating the brain Electrical stimulation of the visual cortex has long been proposed as an approach to restoring vision in blind people. Previous studies positioned electrodes on the surface of the brain and thus required delivery of relatively high currents. However, this approach limits the number of electrodes that can be safely stimulated simultaneously, and such surface electrodes activate several millimeters of cortex, which results in a low spatial resolution. Chen et al. demonstrated that the simultaneous stimulation of multiple intracortical electrodes in the monkey primary visual cortex gives rise to the perception of shape and successive stimulation to the perception of motion (see the Perspective by Beauchamp and Yoshor). This major improvement provides proof of concept for the use of electrical microstimulation to create a form of artificial vision in the blind. Science , this issue p. 1191 ; see also p. 1165

Implantable electrical devices offer a variety of potential diagnostic options and treatments in different medical fields. Especially in the absence of specific drugs, the implantable nerve electrodes (INEs) are one of the main treatments for neurological diseases such as epilepsy, Parkinson’s disease, Alzheimer’s disease, etc. INEs are helpful in studying and regulating nervous system via recording nerve electrical signals or stimulating nerve tissue, but the mechanical mismatch between rigid electrode and soft biological tissue is a critical challenge for long-term implantation. The advances in micromachining technologies and materials have greatly promoted the development of INEs, such as enhanced biocompatibility, reduced foreign body reactions, and structural innovation. In particular, the mechanical performances of flexible implantable electrode and soft biological tissue matched better with less tissue damage. In this review, the implantable flexible nerve electrodes (IFNEs) based on functionalized substrates, intelligent electrodes, and innovative structures are elaborated and discussed. We summarized their various applications in neural prosthesis and neural signal recording. We also discussed the questions and possible methods for developing future IFNEs in the perspective.

Prosthetic hands, today, have anthropomorphic, multifinger design. A common control method uses pattern recognition of electromyogram signals. However, these prostheses do not capture the human hand’s sensory perception, which is critical for prosthesis embodiment and dexterous object manipulation. This problem can be solved by sensorized electronic skin (e-skin) composed of various sensors that transduce sensory percepts such as touch, pressure, temperature, and pain, just as human skin does. This review will present the physiology of the receptors that encode tactile, thermal, nociceptive, and proprioceptive information. The e-skin is designed to mimic these receptors and their responses. We review each sensor subtype, and its design and performance when embedded in the e-skin. Next, we review the spiking response of the receptors, which are then relayed to sensory nerves and encoded by the brain as sensory percepts. The e-skin system is designed to produce neuromorphic or receptorlike spiking activity. Computational models to mimic these sensory nerve signals are presented and then various methods to interface with the nervous system are explored and compared. We conclude the review with the state of the art in e-skin design and deployment in closed-loop applications that demonstrate the benefits of sensory feedback for amputees.

Summary: Most of patients amputated are carrier of a prosthesis, what allows to the user follow realizing part or the whole of his daily activities. We can define the prostheses like an artificial element endowed of some able autonomy to realize the function that would realize the part amputated of the body. Inside the prostheses, the neural prostheses are a system of transduction bidirectional between the organism and the proper prosthesis, which commission to register the biological signals of the body through electrodes. Will carry out a bibliographic review with the aim to know the scientific evidence current about the possible use of the prostheses neurales in amputados. Methods: It was made a revision of the scientific literature between December of 2018 and January of 2019 in the data bases Medline, Cinahl, Web of Science, Pubmed and Scopus and the searched with the terms: “amputation”, “neural prostheses”, “upper extremity” and “lower extremity” and broadened the research whit “upper limb” and “lower limb”. It showed up a total of 15 valid results after applying the criteria of inclusion and exclusion Results: The articles dissected used different types of prosthetic controllers, as they are the electrodes surgically implants, the prosthesis myoelectric the neural interfaces (agonistantagonist), the measurement units inertial or the EMSSA system. Observed that the use of these neural prostheses allowed main functionality to the users of the same, like this like improvements to the hour to realize the movements. Conclusion: Can conclude that the neural prostheses in amputees have a scientific evidence checked in the actuality, being able to contribute big profits to his daily life. However, due to the fact that you treat of technological elements that in the actuality find in roads of development, is not possible to take advantage of to the maximum his performance. Therefore, his optimization would go tied to a main scientific development and technological

OBJECTIVEWireless technology is a novel tool for the transmission of cortical signals. Wireless electrocorticography (ECoG) aims to improve the safety and diagnostic gain of procedures requiring invasive localization of seizure foci and also to provide long-term recording of brain activity for brain-computer interfaces (BCIs). However, no wireless devices aimed at these clinical applications are currently available. The authors present the application of a fully implantable and externally rechargeable neural prosthesis providing wireless ECoG recording and direct cortical stimulation (DCS). Prolonged wireless ECoG monitoring was tested in nonhuman primates by using a custom-made device (the ECoG implantable wireless 16-electrode [ECOGIW-16E] device) containing a 16-contact subdural grid. This is a preliminary step toward large-scale, long-term wireless ECoG recording in humans.METHODSThe authors implanted the ECOGIW-16E device over the left sensorimotor cortex of a nonhuman primate (Macaca fascicularis), recording ECoG signals over a time span of 6 months. Daily electrode impedances were measured, aiming to maintain the impedance values below a threshold of 100 KΩ. Brain mapping was obtained through wireless cortical stimulation at fixed intervals (1, 3, and 6 months). After 6 months, the device was removed. The authors analyzed cortical tissues by using conventional histological and immunohistological investigation to assess whether there was evidence of damage after the long-term implantation of the grid.RESULTSThe implant was well tolerated; no neurological or behavioral consequences were reported in the monkey, which resumed his normal activities within a few hours of the procedure. The signal quality of wireless ECoG remained excellent over the 6-month observation period. Impedance values remained well below the threshold value; the average impedance per contact remains approximately 40 KΩ. Wireless cortical stimulation induced movements of the upper and lower limbs, and elicited fine movements of the digits as well. After the monkey was euthanized, the grid was found to be encapsulated by a newly formed dural sheet. The grid removal was performed easily, and no direct adhesions of the grid to the cortex were found. Conventional histological studies showed no cortical damage in the brain region covered by the grid, except for a single microscopic spot of cortical necrosis (not visible to the naked eye) in a region that had undergone repeated procedures of electrical stimulation. Immunohistological studies of the cortex underlying the grid showed a mild inflammatory process.CONCLUSIONSThis preliminary experience in a nonhuman primate shows that a wireless neuroprosthesis, with related long-term ECoG recording (up to 6 months) and multiple DCSs, was tolerated without sequelae. The authors predict that epilepsy surgery could realize great benefit from this novel prosthesis, providing an extended time span for ECoG recording.

Peripheral neuromodulation boosts health and cognitive benefits in leg amputees

Cochlear implants (CIs) are widely used neural prostheses to restore hearing in hearing impaired patients. The implant's platinum electrodes are comparable to microelectrodes typically used in bio- and chemo-sensors. We show how advanced electrochemical protocols can be used to turn CI electrodes into electrochemical sensors, e.g. for oxygen. The overall goal is to measure the implant's microenvironment and characterize electrode degradation in order to improve implant function and life-time. For the first time, we characterized the performance of CI electrodes as chemical sensors and demonstrated the in vivo measurement of intracochlear oxygen levels with a CI in an animal model.

Refereed/Peer-reviewed Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.

The load transfer from the external prosthesis to the residual limb via the socket can cause significant stress on the soft tissues, leading to irritation and skin ulcers. Osseointegrated bone-anchored prostheses systems create a direct structural and functional connection between the prosthesis and residual skeleton. Up to date, standardized implant systems, surgical techniques, and postoperative rehabilitation protocols have been developed for osseointegrated prostheses for the rehabilitation of amputees (OPRA), which has resulted in better functionality, fewer complications, and a better quality of life for implant recipients. The OPRA implant systems can now incorporate neuromuscular electrodes to facilitate myoelectric control and sensory feedback, which is especially important for upper extremity amputees. The latest development, called the osseointegrated human-machine gateway, allows for permanent implantation of neuromuscular electrodes, which provide long-term stable signals for myoelectric control, independent of limb position or environmental conditions, as well as artificial sensory feedback. In addition, the modular design of this system allows any part to be upgraded or replaced with minimal disturbance to the other components. The osseointegrated implants and the human-machine gateway represent frontiers in amputee rehabilitation.

Intracortical brain-computer interfaces (iBCIs) provide people with paralysis a means to control devices with signals decoded from brain activity. Despite recent impressive advances, these devices still cannot approach able-bodied levels of control. To achieve naturalistic control and improved performance of neural prostheses, iBCIs will likely need to include proprioceptive feedback. With the goal of providing proprioceptive feedback via mechanical haptic stimulation, we aim to understand how haptic stimulation affects motor cortical neurons and ultimately, iBCI control. We provided skin shear haptic stimulation as a substitute for proprioception to the back of the neck of a person with tetraplegia. The neck location was determined via assessment of touch sensitivity using a monofilament test kit. The participant was able to correctly report skin shear at the back of the neck in 8 unique directions with 65% accuracy. We found motor cortical units that exhibited sensory responses to shear stimuli, some of which were strongly tuned to the stimuli and well modeled by cosine-shaped functions. We also demonstrated online iBCI cursor control with continuous skin-shear feedback driven by decoded command signals. Cursor control performance increased slightly but significantly when the participant was given haptic feedback, compared to the purely visual feedback condition.

In this paper, a novel, low-cost multi-electrode array fabrication method has been proposed for epidural spinal cord stimulation (ESCS) to restore motion ability after paralysis due to spinal cord injury (SCI). In this approach, unlike the existing neural prosthesis technologies, costly standard microfabrication processes are eliminated. This makes the proposed electrodes cost-effective and suitable for massive production for clinical applications. Based on the conformability and mechanical compliance of the electrodes and spinal cord tissue, full polydimethylsiloxane (PDMS)-based passive multi-electrode array structure is proposed. The suggested neural electrode array structure consists of PDMS (as substrate) and embedded 100 μm-thick Cu wires (as conductive parts). Despite the fabrication and implementation challenges caused by the low Young modulus of PDMS, its suitable mechanical properties close to those of the spinal cord tissues make PDMS one of the best options. These electrodes were used along with pulse generator circuits providing biphasic pulse waveforms for two channels. Employing the ESCS system caused movement in the paralyzed limbs in an adult male Wistar rat. Our preliminary ESCS studies have shown that such electrode arrays are capable of neuronal stimulation effectively.

Neural prostheses are designed to counter the effects of neurotrauma and restore the fundamental building blocks of human experience including motor action, sensation, and meaningful communication with other individuals. Here, we present an overview of active avenues, open questions, and debated topics in neuroprosthetics, such as targeting the mechanisms of sensorimotor recovery and designing brain interfaces for scalability. We review leading opinions in this thriving field, aiming to inform translational practice toward clinical adoption.

The neural prosthesis under development is designed to improve gait in people with muscle weakness. The strategy is to augment impaired or damaged neural connections between the brain and the muscles that control walking. This third-generation neural prosthesis contains triaxial inertial measurement units (IMUs - accelerometers, gyroscopes, and processing chip) to measure body segment position and force sensitive resistors placed under the feet to detect ground contact. A study was conducted to compare the accuracy of the neural prosthesis using a traditional camera motion capture system as a reference. The IMUs were found to accurately represent the amplitude of the gait cycle components and generally track the motion. However, there are some differences in phase, with the IMUs lagging the actual motion. Phase lagged by about 10 degrees in the ankle and by about 5 degrees in the knee. Error of the neural prosthesis varied over the gait cycle. The average error for the ankle, knee and hip were 6°, 8°, and 9°, respectively. Testing showed that the neural prosthesis was able to capture the general shape of the joint angle curves when compared to a commercial camera motion capture system. In the future, measures will be taken to reduce lag in the gyroscope and reduce jitter in the accelerometer so that data from both sensors can be combination to obtain more accurate readings.

Microelectrodes for pain management, neural prosthesis or assistances have a huge medical demand, such as the application of pain management chip or retinal prosthesis addressed on age-related macular degeneration (AMD) and the retinitis pigmentosa (RP). Due to lifelong implanted in human body and direct adhesion of neural tissues, the electrodes and associated insulation materials should possess an ideal bio-compatibility, including non-cytotoxicity and no safety concern elicited by immune responses. Our goal intended to develop retinal prosthesis, an electrical circuit chip used for assisting neural electrons transmission on retina and ameliorating the retinal disability. Therefore, based on the ISO 10993 guidance for implantable medical devices, the electrode prosthesis with insulation material has to conduct bio-compatibility assessment including cytotoxicity, hemolysis, (skin) irritation and pathological implantation examinations. In this study, we manufactured inter-digitated electrode (IDE) chips mimic the electrode prosthesis through photolithography. The titanium and platinum composites were deposited onto a silicon wafer to prepare an electric circuit to mimic the electrode used in retinal prosthesis manufacture, which further be encapsulated to examine the bio-compatibility in compliance with ISO 10993 and ASTM guidance specifically for implantable medical devices. Parylene-C, polyimide and silicon carbide were selected as materials for electrode encapsulation in comparison. Our data revealed parylene-C coating showed a significant excellence on bio-insulation and bio-compatibility specifically addressed on implantable neuron stimulatory devices and provided an economic procedure to package the electrode prosthesis. Therefore, parylene C encapsulation should serve as a consideration for future application on retinal prosthesis manufacture and examination.

SUMMARYModern brain-machine interfaces can return function to people with paralysis, but current hand neural prostheses are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a novel task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined, presenting separate targets for each group. During online brain control, the ReFIT Kalman filter demonstrated the capability of individuating movements of each finger group with high performance, enabling a nonhuman primate to acquire two targets simultaneously at 1.95 targets per second, resulting in an average information throughput of 2.1 bits per second. To understand this result, we performed single unit tuning analyses. Cortical neurons were active for movements of an individual finger group, combined movements of both finger groups, or both. Linear combinations of neural activity representing individual finger group movements predicted the neural activity during combined finger group movements with high accuracy, and vice versa. Hence, a linear model was able to explain how cortical neurons encode information about multiple dimensions of movement simultaneously. Additionally, training ridge regressing decoders with independent component movements was sufficient to predict untrained higher-complexity movements. Our results suggest that linear decoders for brain-machine interfaces may be sufficient to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

In the innovative neural prostheses, the biological cell assemblies of the biological nervous system can be replaced by artificial organs, which makes the idea of dynamically interface biological neurons even more urgent. To mimic and investigate the activity of biological neural networks, many different architectures and technologies in the field of neuromorphic have been developed at present. When structuring simple neuron models, researchers use Field programmable gate arrays (FPGAs) to obtain better accuracy and real-time performance. This paper uses FPGAs to achieve the circuit design of the neuron model, such that based on the biologically plausible the quadratic spiking neuron model, can simulate the neuron spiking behaviors of thalamus neurons and hippocampal CA1 pyramidal neurons. After the FPGA hardware architecture of the neuron model is designed and implemented, this model can better simulate the spiking behaviors observed in biological neurons.

Patients suffering from chronic facial palsy are frequently impaired by severe life-long dysfunctions. Thus, the loss of the ability to close eyes rapidly and completely bears the risk of corneal damages. Moreover, the loss of smile and an altered facial expression imply psychological stress and impede a healthy social life. Since surgical and conservative treatments frequently do not solve many problems sufficiently, closed-loop neural prosthesis are considered as feasible approach. For it, amongst others a reliable detection of the currently executed facial movement is necessary. In our proof of concept study, we propose a data-driven feature extraction for classifying eye closures and smile based on intramuscular EMGs from orbicularis oculi and zygomaticus muscles of the patient’s palsy side. The data-adaptive nature of the approach enables a flexible applicability to different muscles and subjects without patient-or muscle-specific adaptations.

Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system behavior. In this study, a recent multi-electrode technology allows the record of the simultaneous neuron activities in behaving animals. Intra-cortical data are processed according to these steps: spike detection and sorting, than desired action extraction from the rate of the obtained signal. We focus on the following important questions about (i) the possibility of linking the brain signal time events with some time-delayed mapping tools; (ii) the use of some suitable inputs than others for the decoder; (iii) a consideration of separated data or a special representation founded on multi-dimensional statistics. This paper concentrates mostly on the analysis of parallel spike train when certain critical hypotheses are ignored by the data for the working method. We have made efforts to define explicitly whether the underlying hypotheses are actually achieved. In this paper, we propose an algorithm to define the embedded memory order of NARX recurrent neural networks to the hand trajectory tracking process. We also demonstrate that this algorithm can improve performance on inference tasks.

Restoring tactile sensations in upper limb neuro-prosthetic users is an important challenge to improve amputee quality of life. Neural prostheses are based on neural interfaces that allow delivering direct stimuli to the nerve fibers. Different kinds of neural electrodes, characterized by different properties as regards geometry or current stimulation parameters, were proposed in the literature. Studying the interaction between electrode stimulation and nerve fibers is fundamental for evaluating its ability to activate axon fibers and the electric potential distribution in the nerve. In this paper, the ds-FILE electrode is considered in the FEM simulation to study its interaction with the nerve fibers. The results can be very useful for the advancement of more realistic tactile sensations in amputee subjects from two point of view: the efficacy of the stimulation, that is related to the activation of axons subjected to the electrical stimulation, and the safety of the stimulation, related from a first consideration to the current intensity and waveform used. So in this paper are considered the results of three type of waveform that are the more safety as reported in literature [1] and they are comparedThe research aims to study, by means of a FEM-Neuron computational model, the axon fibers activation by means of the intraneural stimulation using different types of stimulus waveforms. The obtained results show that, using a biphasic charge unbalanced stimulus, the threshold to activate all the fibers considered in a location near an active site of the electrode is lower than the threshold found using biphasic charge balanced stimuli. This is an important result because the stimulation is equally efficient using low current amplitude.

Control of human arm in reaching task is a result of complex neural interaction involving central nervous and musculoskeletal system, where, group of muscle activation are planned through synergistic and coordinated recruitment, often to reach an optimal strategy. Aim of this paper is to explore muscle synergy distribution on several reaching task of similar elbow trajectory but changing shoulder configuration. A musculoskeletal model of human arm comprising shoulder, elbow and wrist joint have been designed and is used to calculate muscle activation required to perform three specific reaching tasks. Muscle synergy have been computed on the simulated activation to find a relation between synergy and energy requirement with the change of rotation and elevation of shoulder and its effect on the motion path of the elbow joint. These findings may help to define optimal joint configuration for a planned range of motion during rehabilitation exercises and also in developing neural prosthesis and myoelectric interfaces for efficient arm motion control.

Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance decoding performance of neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The highest accuracy achieved was 87% using 13 features that were extracted from both high and low-frequency (LF) bands of DCN signals. In general, high-frequency (HF) features contained the most information about peripheral somatosensory events, but when features were acquired from short time-windows, classification accuracy was significantly improved by adding LF features to the feature set. We found that proprioception-dominated stimuli generalize across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to neural decoding changes over the time-course of dynamic somatosensory events. These findings may inform the biomimetic design of artificial stimuli that can activate the DCN to substitute somatosensory feedback. Although, we investigated somatosensory structures, the feature set we investigated may also prove useful for decoding other (e.g., motor) neural signals.

Electroencephalogram (EEG) based brain-computer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49% for motor execution (ME) and 40.36% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. This result is encouraging, and the proposed method could potentially be used in future applications such as a BCI-driven robot control for handling various daily use objects., Accepted to IEEE EMBC 2020

Electrocorticography (ECoG), electrophysiological recording from the pial surface of the brain, is a critical measurement technique for clinical neurophysiology, basic neurophysiology studies, and demonstrates great promise for the development of neural prosthetic devices for assistive applications and the treatment of neurological disorders. Recent advances in device engineering are poised to enable orders of magnitude increase in the resolution of ECoG without comprised measurement quality. This enhancement in cortical sensing enables the observation of neural dynamics from the cortical surface at the micrometer scale. While these technical capabilities may be enabling, the extent to which finer spatial scale recording enhances functionally relevant neural state inference is unclear. We examine this question by employing a high-density and low impedance 400 μm pitch microECoG (μECoG) grid to record neural activity from the human cortical surface during cognitive tasks. By applying machine learning techniques to classify task conditions from the envelope of high-frequency band (70–170Hz) neural activity collected from two study participants, we demonstrate that higher density grids can lead to more accurate binary task condition classification. When controlling for grid area and selecting task informative sub-regions of the complete grid, we observed a consistent increase in mean classification accuracy with higher grid density; in particular, 400 μm pitch grids outperforming spatially sub-sampled lower density grids up to 23%. We also introduce a modeling framework to provide intuition for how spatial properties of measurements affect the performance gap between high and low density grids. To our knowledge, this work is the first quantitative demonstration of human sub-millimeter pitch cortical surface recording yielding higher-fidelity state estimation relative to devices at the millimeter-scale, motivating the development and testing of μECoG for basic and clinical neurophysiology as well as towards the realization of high-performance neural prostheses.

I've got a feeling Sensory (or afferent) nerves bring sensations of touch, pain, or temperature variation to the central nervous system and brain. Using the tools and materials of organic electronics, Kim et al. combined a pressure sensor, a ring oscillator, and an ion gel–gated transistor to form an artificial mechanoreceptor (see the Perspective by Bartolozzi). The combination allows for the sensing of multiple pressure inputs, which can be converted into a sensor signal and used to drive the motion of a cockroach leg in an oscillatory pattern. Science , this issue p. 998 ; see also p. 966

Modulation of nerve signals from the peripheral nervous system (PNS) is a growing field of neurotechnology for therapeutic effects of the human body and for interfacing with neural prostheses. For this, neural interfaces, that provide the basis for direct communication with neuron tissues and mapping neural signals, are preferentially developed. Even though various types of peripheral nerve interfaces have been developed for many years, advanced neural interfaces need to be developed in conjunction with cutting-edge technology for the modulation of the human body and advanced neural prostheses. This paper reviews the evolution of peripheral neural interfaces (PNI) and their applications. The emerging requirements of the next-generation PNI are also explored.

Background: The cochlear implant has become the standard of care for severe or worse losses in hearing and indeed has produced the first substantial restoration of a lost or absent human sense using a medical intervention. However, the devices are not perfect and many efforts to narrow the remaining gaps between prosthetic and normal hearing are underway. Objective: To assess the present status of cochlear implants and to describe possibilities for improving them. Results: The present-day devices work well in quiet conditions for the great majority of users. However, not all users have high levels of speech reception in quiet and nearly all users struggle with speech reception in typically noisy acoustic environments. In addition, perception of sounds more complex than speech, such as most music, is generally poor unless residual hearing at low frequencies can be stimulated acoustically in conjunction with the electrical stimuli provided by the implant. Possibilities for improving the present devices include increasing the spatial specificity of neural excitation by reducing masking effects or with new stimulus modes; prudent pruning of interfering or otherwise detrimental electrodes from the stimulation map; a further relaxation in the criteria for implant candidacy, based on recent evidence from persons with high levels of residual hearing and to allow many more people to benefit from cochlear implants; and “top down” or “brain centric” approaches to implant designs and applications. Conclusions: Progress in the development of the cochlear implant and related treatments has been remarkable but room remains for improvements. The future looks bright as there are multiple promising possibilities for improvements and many talented teams are pursuing them. Keywords: Auditory prosthesis, Cochlear implant, Cochlear prosthesis, Deafness, Neural prosthesis

The cochlear implant (CI) is the most successful neural prosthesis, restoring the sensation of sound in people with severe-to-profound hearing loss by electrically stimulating the cochlear nerve. Existing CIs have an external, visible unit, and an internal, surgically-placed unit. There are significant challenges associated with the external unit, as it has limited utility and CI users often report a social stigma associated with prosthesis visibility. A fully-implantable CI (FICI) would address these issues. However, the volume constraint imposed on the FICI requires less power consumption compared to today’s CI. Because neural stimulation by CI electrodes accounts for up to 90% of power consumption, reduction in stimulation power will result directly in CI power savings. To determine an energy-efficient waveform for cochlear nerve stimulation, we used a genetic algorithm approach, incorporating a computational model of a single mammalian myelinated cochlear nerve fiber coupled to a stimulator-electrode-tissue interface. The algorithm’s prediction was tested in vivo in human CI subjects. We find that implementation of a non-rectangular biphasic neural stimulation waveform may result in up to 25% charge savings and energy savings within the comfortable range of hearing for CI users. The alternative waveform may enable future development of a FICI.

Amputees who wish to rid themselves of a phantom limb must weaken the neural representation of the absent limb. Conversely, amputees who wish to replace a lost limb must assimilate a neuroprosthetic with the existing neural representation. Whether we wish to remove a phantom limb or assimilate a synthetic one, we will benefit from knowing more about the developmental process that enables embodiment. A potentially critical contributor to that process is the spontaneous activity - in the form of limb twitches - that occurs exclusively and abundantly during active (REM) sleep, a particularly prominent state in early development. The sensorimotor circuits activated by twitching limbs, and the developmental context in which activation occurs, could provide a roadmap for creating neuroprosthetics that feel as if they are part of the body.

High-fidelity measurements of neural activity can enable advancements in our understanding of the neural basis of complex behaviors such as speech, audition, and language, and are critical for developing neural prostheses that address impairments to these abilities due to disease or injury. We develop a novel high resolution, thin-film micro-electrocorticography (micro-ECoG) array that enables high-fidelity surface measurements of neural activity from songbirds, a well-established animal model for studying speech behavior. With this device, we provide the first demonstration of sensory-evoked modulation of surface-recorded single unit responses. We establish that single unit activity is consistently sensed from micro-ECoG electrodes over the surface of sensorimotor nucleus HVC (used as a proper name) in anesthetized European starlings, and validate responses with correlated firing in single units recorded simultaneously at surface and depth. The results establish a platform for high-fidelity recording from the surface of subcortical structures that will accelerate neurophysiological studies, and development of novel electrode arrays and neural prostheses.

Current neural prostheses can restore limb movement to tetraplegic patients by translating brain signals coding movements to control a variety of actuators. Fast and accurate somatosensory feedback is essential for normal movement, particularly dexterous tasks, but is currently lacking in motor neural prostheses. Attempts to restore somatosensory feedback have largely focused on cortical stimulation which, thus far, have succeeded in eliciting minimal naturalistic sensations. Yet, a question that deserves more attention is whether the cortex is the best place to activate the central nervous system to restore somatosensation. Here, we propose that the brainstem dorsal column nuclei are an ideal alternative target to restore somatosensation. We review some of the recent literature investigating the dorsal column nuclei functional organization and neurophysiology and highlight some of the advantages and limitations of the dorsal column nuclei as a future neural prosthetic target. Recent evidence supports the dorsal column nuclei as a potential neural prosthetic target, but also identifies several gaps in our knowledge as well as potential limitations which need to be addressed before such a goal can become reality.

Intracortical microelectrodes are being developed to both record and stimulate neurons to understand brain circuitry or restore lost functions. However, the success of these probes is hampered partly due to the inflammatory host tissue responses to implants. To minimize the foreign body reactions, L1, a brain derived neuronal specific cell adhesion molecule, has been covalently bound to the neural electrode array surface. Here we evaluated the chronic recording performance of L1-coated silicon based laminar neural electrode arrays implanted into V1m cortex of mice. The L1 coating enhanced the overall visually evoked single-unit (SU) yield and SU amplitude, as well as signal-to-noise-ratio (SNR) in the mouse brain compared to the uncoated arrays across the 0–1500 μm depth. The improvement in recording is most dramatic in the hippocampus region, where the control group showed severe recording yield decrease after one week, while the L1 implants maintained a high SU yield throughout the 16 weeks. Immunohistological analysis revealed significant increases of axonal and neuronal density along with significantly lowered microglia activation around the L1 probe after 16 weeks. These results collectively confirm the effectiveness of L1 based biomimetic coating on minimizing inflammatory tissue response and improving neural recording quality and longevity. Improving chronic recording will benefit the brain-computer interface technologies and neuroscience studies involving chronic tracking of neural activities.

Handwriting is an advanced motor skill and one of the key developments in human culture. Here we show that handwriting can be decoded—offline and online—from electromyographic (EMG) signals recorded from multiple hand and forearm muscles. We convert EMGs into continuous handwriting traces and into discretely decoded font characters. For this purpose, we use Wiener and Kalman filters, and machine learning algorithms. Our approach is applicable to clinical neural prostheses for restoration of dexterous hand movements, and to medical diagnostics of neural disorders that affect handwriting. We also propose that handwriting could be decoded from cortical activity, such as the activity recorded with electrocorticography (ECoG).

Technologies that restore movement and the sense of touch are helping people to overcome the physical effects of stroke and spinal-cord injury. Technologies that restore movement and the sense of touch are helping people to overcome the physical effects of stroke and spinal-cord injury.

Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, restoration of somatosensory feedback and advances in neural decoding of motor control-related brain signals are required. We used a machine-learning approach to assess signal features for their capacity to enhance decoding performance of neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei in urethane-anaesthetised rats. We determined signal features that are highly informative for decoding somatosensory stimuli, yet these appear underutilised in neuroprosthetic applications. We found that proprioception-dominated stimuli generalise across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to neural decoding changes over a time-course of dynamic somatosensory events. These findings may improve neural decoding for various applications including novel neuroprosthetic design.

Introduction: The vestibular implant could become a clinically useful device in the near future. This study investigated the feasibility of restoring the high-frequency dynamic visual acuity (DVA) with a vestibular implant, using the functional Head Impulse Test (fHIT). Methods: A 72-year-old female, with bilateral vestibulopathy and fitted with a modified cochlear implant incorporating three vestibular electrodes (MED-EL, Innsbruck, Austria), was available for this study. Electrical stimulation was delivered with the electrode close to the lateral ampullary nerve in the left ear. The high-frequency DVA in the horizontal plane was tested with the fHIT. After training, the patient underwent six trials of fHIT, each with a different setting of the vestibular implant: (1) System OFF before stimulation; (2) System ON, baseline stimulation; (3) System ON, reversed stimulation; (4) System ON, positive stimulation; (5) System OFF, without delay after stimulation offset; and (6) System OFF, 25 min delay after stimulation offset. The percentage of correct fHIT scores for right and left head impulses were compared between trials. Results: Vestibular implant stimulation improved the high-frequency DVA compared to no stimulation. This improvement was significant for “System ON, baseline stimulation” (p = 0.02) and “System ON, positive stimulation” (p < 0.001). fHIT scores changed from 19 to 44% (no stimulation) to maximum 75–94% (System ON, positive stimulation). Conclusion: The vestibular implant seems capable of improving the high-frequency DVA. This functional benefit of the vestibular implant illustrates again the feasibility of this device for clinical use in the near future.

Upper limb prostheses are specialized tools, and skilled operation is learned by amputees over time. Recently, neural prostheses using implanted peripheral nerve interfaces have enabled advances in artificial somatosensory feedback that can improve prosthesis outcomes. However, the effect of sensory learning on artificial somatosensation has not been studied, despite its known influence on intact somatosensation and analogous neuroprostheses. Sensory learning involves changes in the perception and interpretation of sensory feedback and may further influence functional and psychosocial outcomes. In this mixed methods case study, we examined how passive learning over 115 days of home use of a neural-connected, sensory-enabled prosthetic hand influenced perception of artificial sensory feedback in a participant with transradial amputation. We examined perceptual changes both within individual days of use and across the duration of the study. At both time scales, the reported percept locations became significantly more aligned with prosthesis sensor locations, and the phantom limb became significantly more extended toward the prosthesis position. Similarly, the participant’s ratings of intensity, naturalness, and contact touch significantly increased, while his ratings of vibration and movement significantly decreased across-days for tactile channels. These sensory changes likely resulted from engagement of cortical plasticity mechanisms as the participant learned to use the artificial sensory feedback. We also assessed psychosocial and functional outcomes through surveys and interviews, and found that self-efficacy, perceived function, prosthesis embodiment, social touch, body image, and prosthesis efficiency improved significantly. These outcomes typically improved within the first month of home use, demonstrating rapid benefits of artificial sensation. Participant interviews indicated that the naturalness of the experience and engagement with the prosthesis increased throughout the study, suggesting that artificial somatosensation may decrease prosthesis abandonment. Our data showed that prosthesis embodiment was intricately related to naturalness and phantom limb perception, and that learning the artificial sensation may have modified the body schema. As another indicator of successfully learning to use artificial sensation, the participant reported the emergence of stereognosis later in the study. This study provides the first evidence that artificial somatosensation can undergo similar learning processes as intact sensation and highlights the importance of sensory restoration in prostheses.

A number of peripheral nerve interfaces for nerve stimulation and recording exist for the purpose of controlling neural prostheses, each with a set of advantages and disadvantages. The ultimate goal of neural prostheses is a seamless bi-directional communication between the peripheral nervous system and the prosthesis. Here, we developed an interfacing electrode array, the "cuff and sieve electrodes" (CASE), integrating microfabricated cuff and sieve electrodes to a single unit, to decrease the weaknesses faced by these electrode designs in isolation. This paper presents the design and fabrication of CASE with ex vivo and in vivo testing towards chronic application.Electroplating on electrode sites was performed to improve electrical properties of CASE. The surface morphology and chemical compound were characterized using scanning electron microscopy and energy-dispersive spectroscopy, respectively. Electrochemical impedance spectroscopy and cyclic voltammetry were performed to evaluate the electrical properties of CASE and determine viability for in vivo applications. Terminal CASE implantations were performed in a rat sciatic transection model to test the ease of implantation and capacity to write sensory information into the biological system.The modified platinum film resulted in reducing impedance magnitude (9.18 kΩ and 2.27 kΩ) and increasing phase angle (over 70°). CASE stimulation of the sciatic nerve at different amplitudes elicited significantly different cortical responses (p0.005) as demonstrated by somatosensory evoked potentials, recorded via micro-electrocorticography.The ability to elicit cortical responses from sciatic nerve stimulation demonstrates the proof of concept for both the implantation and chronic monitoring of CASE interfaces for innovative prosthetic control.

The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked. We also aimed to explore the robustness of DCN SFs and map their relative information content across the brainstem surface. DCN surface potentials were recorded from urethane-anesthetized Wistar rats during sural and peroneal nerve electrical stimulation. Five salient SFs were extracted from each recording electrode of a seven-electrode array. We used a machine learning approach to quantify and rank information content contained within DCN surface-potential signals following peripheral nerve activation. Machine-learning of SF and electrode position combinations was quantified to determine a hierarchy of information importance for resolving the peripheral origin of nerve activation. A supervised back-propagation artificial neural network (ANN) could predict the nerve from which a response was evoked with up to 96.8 ± 0.8% accuracy. Guided by feature-learnability, we maintained high prediction accuracy after reducing ANN algorithm inputs from 35 (5 SFs from 7 electrodes) to 6 (4 SFs from one electrode and 2 SFs from a second electrode). When the number of input features were reduced, the best performing input combinations included HF and LF features. Feature-learnability also revealed that signals recorded from the same midline electrode can be accurately classified when evoked from bilateral nerve pairs, suggesting DCN surface activity asymmetry. Here we demonstrate a novel method for mapping the information content of signal patterns across the DCN surface and show that DCN SFs are robust across a population. Finally, we also show that the DCN is functionally asymmetrically organized, which challenges our current understanding of somatotopic symmetry across the midline at sub-cortical levels.

Osseointegration, which describes the direct biological connection between bone and metal, has emerged as a promising method of increasing function and mobility in amputees. This technology holds significant potential, including the ability to harness an integrated neuromuscular interface to allow for volitional motor and sensory control of an external neural prosthesis. While orthopaedic osseointegration has been underway internationally for many years with a variety of implant systems, the practice is still in its early phases in the United States. Here we present the early outcomes of the first American trial for the Osseoanchored Prosthesis for the Rehabilitation of Amputees (OPRA). Our study demonstrates that one year following osseointegration, patients experience a significant improvement in function and health-related outcomes.

The integration of Brain-Computer-Interfaces (BCI) into rehabilitation research is a promising approach that may substantially impact the rehabilitation success. Yet, there is still significant challenges that needs to be addressed before the BCI technology can be fully used effectively in a clinical setting as a neural prosthesis for motor impaired users. As it is still unknown whether the conventional BCI induction strategies that use different the types of stimuli and/or mental tasks induce cortical reorganization for disabled users. This paper presents a design and evaluation of a real-time Near-Infrared Spectroscopy (NIRS) based BCI protocol to control an external haptic device, and an interesting source of brain signals that may convey complementary information for inducing neuroplasticity. The protocol is based on the ideas derived from Mirror-based Therapy (MT) in which subjects not only perform literal motor imagery tasks but also combine their intents with visual action observation of a related motor imagery task. The NIRS-BCI system then commands a haptic device in real-time to move in opposing directions of leftward and rightward movement. We also compare the proposed protocol to the conventional limb motor imagery task and verify its efficacy with online decoding accuracies up to 94.99%. The initial validation of the experimental setup was done with seven healthy subjects. Nonetheless we contend that the design of the current NIRS-BCI method hold promise with patient populations for effective stroke rehabilitation therapy, because the beneficial effects of MT alone in post-stroke recovery has already been manifested in the literature.

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain–machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENTWhen generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis.

Electromyography (EMG) is a well known technique used for recording electrical activity produced by human muscles. In the last few decades, EMG signals are used as a control input for prosthetic hands. There are several multifunctional myoelectric prosthetic hands for amputees on the market, but so forth, none of these devices permits the natural control of more than two degrees of freedom. In this paper we present our implementation of the pattern classification using custom made components (electrodes and an embedded EMG amplifier). The components were evaluated in offline and online tests, in able bodied as well as amputee subjects. This type of control is based on computing the time domain features of the EMG signals recorded from the forearm and using these features as input for a Linear Discriminant Analysis (LDA) classifier estimating the intention of the prosthetic user. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III - 41007]