The digital motor output: a conceptual framework for a meaningful clinical performance metric for a motor neuroprosthesis

In recent years, the majority of the population has become increasingly reliant on continuous and independent control of smart devices to conduct activities of daily living. Upper extremity movement is typically required to generate the motor outputs that control these interfaces, such as rapidly and accurately navigating and clicking a mouse, or activating a touch screen. For people living with tetraplegia, these abilities are lost, significantly compromising their ability to interact with their environment. Implantable brain computer interfaces (BCIs) hold promise for restoring lost neurologic function, including motor neuroprostheses (MNPs). An implantable MNP can directly infer motor intent by detecting brain signals and transmitting the motor signal out of the brain to generate a motor output and subsequently control computer actions. This physiological function is typically performed by the motor neurons in the human body. To evaluate the use of these implanted technologies, there is a need for an objective measurement of the effectiveness of MNPs in restoring motor outputs. Here, we propose the concept of digital motor outputs (DMOs) to address this: a motor output decoded directly from a neural recording during an attempted limb or orofacial movement is transformed into a command that controls an electronic device. Digital motor outputs are diverse and can be categorized as discrete or continuous representations of motor control, and the clinical utility of the control of a single, discrete DMO has been reported in multiple studies. This sets the stage for the DMO to emerge as a quantitative measure of MNP performance.

Challenges and Perspectives on Impulse Radio-Ultra-Wideband Transceivers for Neural Recording Applications

Brain-machine interfaces (BMI) are widely adopted in neuroscience investigations and neural prosthetics, with sensing channel counts constantly increasing. These Investigations place increasing demands for high data rates and low-power implantable devices despite high tissue losses. The Impulse radio ultra-wideband (IR-UWB), a revived wireless technology for short-range radios, has been widely used in various applications. Since the requirements and solutions are application-oriented, in this review paper we focus on neural recording implants with high-data rates and ultra-low power requirements. We examine in detail the working principle, design methodology, performance, and implementations of different architectures of IR-UWB transceivers in a quantitative manner to draw a deep comparison and extract the bottlenecks and possible solutions concerning the dedicated application. Our analysis shows that current solutions rely on enhanced or combined modulation techniques to improve link margin. An in-depth study of prior-art publications that achieved Gbps data rates concludes that edge-combination architecture and non-coherent detectors are remarkable for transmitter and receiver, respectively. Although the aim to minimize power and improve data rate - defined as energy efficiency (pJ/b) - extending communication distance despite high tissue losses and limited power budget, good narrow-band interference (NBI) tolerance coexisted in the same frequency band of UWB systems, and compatibility with energy harvesting designs are among the critical challenges remained unsolved. Furthermore, we expect that the combination of artificial intelligence (AI) and the inherent advantages of UWB radios will pave the way for future improvements in BMIs.

The efficacy of hybrid neuroprostheses in the rehabilitation of upper limb impairment after stroke, a narrative and systematic review with a meta-analysis

BACKGROUND

Paresis of the upper limb (UL) is the most frequent impairment after a stroke. Hybrid neuroprostheses, i.e., the combination of robots and electrical stimulation, have emerged as an option to treat these impairments.

METHODS

To give an overview of existing devices, their features, and how they are linked to clinical metrics, four different databases were systematically searched for studies on hybrid neuroprostheses for UL rehabilitation after stroke. The evidence on the efficacy of hybrid therapies was synthesized.

RESULTS

Seventy-three studies were identified, introducing 32 hybrid systems. Among the most recent devices (n = 20), most actively reinforce movement (3 passively) and are typical exoskeletons (3 end-effectors). If classified according to the International Classification of Functioning, Disability and Health, systems for proximal support are expected to affect body structures and functions, while the activity and participation level are targeted when applying Functional Electrical Stimulation distally plus the robotic component proximally. The meta-analysis reveals a significant positive effect on UL functions (p < 0.001), evident in a 7.8-point Mdiff between groups in the Fugl-Meyer assessment. This positive effect remains at the 3-month follow-up (Mdiff  = 8.4, p < 0.001).

CONCLUSIONS

Hybrid neuroprostheses have a positive effect on UL recovery after stroke, with effects persisting at least three months after the intervention. Non-significant studies were those with the shortest intervention periods and the oldest patients. Improvements in UL functions are not only present in the subacute phase after stroke but also in long-term chronic stages. In addition to further technical development, more RCTs are needed to make assumptions about the determinants of successful therapy.

Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.

Dense, 11 V-Tolerant, Balanced Stimulator IC with Digital Time-Domain Calibration for 100 nA Error

This article presents a multichannel neurostimulator implementing a novel charge balancing technique to achieve maximal integration. Safe neurostimulation demands accurate charge balancing of the stimulation waveforms to prevent charge build-up on the electrode-tissue interface. We propose digital time-domain calibration (DTDC), which adjusts the second phase of the biphasic stimulation pulses digitally, based on a one-time characterization of all stimulator channels with an on-chip ADC. Accurate control of the stimulation current amplitude is loosened in exchange for time-domain corrections, relieving circuit matching constraints and consequentially saving channel area. A theoretical analysis of DTDC is presented, establishing expressions for the required time resolution and the new, relaxed circuit matching constraints. To validate the DTDC principle, a 16-channel stimulator was implemented in 65 nm CMOS, requiring only 0.0141 mm 2 area/channel. Despite being implemented in a standard CMOS technology, 10.4 V compliance is achieved for compatibility with high-impedance microelectrode arrays typical for high-resolution neural prostheses. To the authors' knowledge, this is the first stimulator in a 65 nm low-voltage process achieving over 10 V output swing. Measurements after calibration show the DC error is successfully reduced below 96 nA on all channels. Static power consumption is 20.3 µW/channel.

A high-performance speech neuroprosthesis

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text1,2 or sound3,4. Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1-7. Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant-who can no longer speak intelligibly owing to amyotrophic lateral sclerosis-achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI2) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant's attempted speech was decoded  at 62 words per minute, which is 3.4 times as fast as the previous record8 and begins to approach the speed of natural conversation (160 words per minute9). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.

Neurorobotics and neuroprostheses: Towards a new anatomy

The idea of this Special Issue arose from the technological advances in bionic, robotic, and neural rehabilitation systems and the common need to comprehend in detail how human anatomical structures can be replicated or controlled. Motor control theories, among others, include the generalized control program theory, the equilibrium point hypothesis, or the optimal control approach in which neural commands to the muscles are a result of the central nervous system solving an optimization problem for a specific cost function. No matter the alternative interpretation selected to replicate biological control of human movements, artificial "anatomies" should consider not only motor capabilities from the central nervous system but integrate bioinspired mechanical features (such as compliance) in artificial limbs. The development of wearable robotics and neuroprosthetic systems for human movement compensation and control is naturally inspired by human anatomy and biology. Cutting-edge technological advances in the field of biomedical and neural engineering are bringing us more and more close to a new artificial anatomy with which humans could augment their motor capabilities or replace them after they are compromised. Either augmentative/assistive or rehabilitation technologies in the near future will require engineering solutions based on novel approaches to create usable neurorobotic and neuroprosthetic systems for the most relevant societal needs.

Conformable Electrode Arrays for Wearable Neuroprostheses

Wearable electrode arrays can selectively stimulate muscle groups by modulating their shape, size, and position over a targeted region. They can potentially revolutionize personalized rehabilitation by being noninvasive and allowing easy donning and doffing. Nevertheless, users should feel comfortable using such arrays, as they are typically worn for an extended time period. Additionally, to deliver safe and selective stimulation, these arrays must be tailored to a user's physiology. Fabricating customizable electrode arrays needs a rapid and economical technique that accommodates scalability. By leveraging a multilayer screen-printing technique, this study aims to develop personalizable electrode arrays by embedding conductive materials into silicone-based elastomers. Accordingly, the conductivity of a silicone-based elastomer was altered by adding carbonaceous material. The 1:8 and 1:9 weight ratio percentages of carbon black (CB) to elastomer achieved conductivities between 0.0021-0.0030 S cm^-1 and were suitable for transcutaneous stimulation. Moreover, these ratios maintained their stimulation performance after several stretching cycles of up to 200%. Thus, a soft, conformable electrode array with a customizable design was demonstrated. Lastly, the efficacy of the proposed electrode arrays to stimulate hand function tasks was evaluated by in vivo experiments. The demonstration of such arrays encourages the realization of cost-effective, wearable stimulation systems for hand function restoration.

A Comparison of Delay-and-Add and Maximum Likelihood Estimation for Velocity-Selective Recording Using Multi-Electrode Cuffs

Extracting information from the peripheral nervous system with implantable devices remains a significant challenge that limits the advancement of closed-loop neural prostheses. Linear electrode arrays can record neural signals with both temporal and spatial selectivity, and velocity selective recording using the delay-and-add algorithm can enable classification based on fibre type. The maximum likelihood estimation method also measures velocity and is frequently used in electromyography but has never been applied to electroneurography. Therefore, this study compares the two algorithms using in-vivo recordings of electrically evoked compound action potentials from the ulnar nerve of a pig. The performance of these algorithms was assessed using the velocity quality factor (Q-factor), computational time and the influence of the number of channels. The results show that the performance of both algorithms is significantly influenced by the number of channels in the recording array, with accuracies ranging from 77% with only two channels to 98% for 11 channels. Both algorithms were comparable in accuracy and Q-factor for all channels, with the delay-and-add having a slight advantage in the Q-factor.

Brain implant enables man in locked-in state to communicate

Despite complete paralysis from amyotrophic lateral sclerosis, person used neural signals to spell out thoughts.

A Wireless Power and Data Transfer IC for Neural Prostheses Using a Single Inductive Link With Frequency-Splitting Characteristic

This paper presents a frequency-splitting-based wireless power and data transfer IC that simultaneously delivers power and forward data over a single inductive link. For data transmission, frequency-shift keying (FSK) is utilized because the FSK modulation scheme supports continuous wireless power transmission without disruption of the carrier amplitude. Moreover, the link that manifests the frequency-splitting characteristic due to a close distance between coupled coils provides wide bandwidth for data delivery without degrading the quality factors of the coils. It results in large power delivery, high data rate, and high power transfer efficiency. The presented IC fabricated in a 180-nm BCD process simultaneously achieves up-to-115-mW wireless power delivery to the load and 2.5-Mb/s downlink data rate over the single inductive link. The measured overall power efficiency from the DC power supply at the transmitter module to the load at the receiver module reaches 56.7 % at its maximum, and the bit error rate is lower than 10 ^-6 at 2.5 Mb/s. As a result, the figure of merit (FoM) for data transmission is enhanced by 2 times, and the FoM for power delivery is improved by 38.7 times compared to prior state-of-the-arts using a single inductive link.

Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.

Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria

BACKGROUND

Technology to restore the ability to communicate in paralyzed persons who cannot speak has the potential to improve autonomy and quality of life. An approach that decodes words and sentences directly from the cerebral cortical activity of such patients may represent an advancement over existing methods for assisted communication.

METHODS

We implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke. Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words. We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them.

RESULTS

We decoded sentences from the participant's cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%. In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period.

CONCLUSIONS

In a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model. (Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149.).

Continuous sensorimotor rhythm based brain computer interface learning in a large population

Brain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheel-chairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks. The current dataset presents one of the largest and most complex SMR-BCI datasets publicly available to date and should be useful for the development of improved algorithms for BCI control.

Noninvasive Neuroprosthesis Promotes Cardiovascular Recovery After Spinal Cord Injury

Spinal cord injury (SCI) leads to severe impairment in cardiovascular control, commonly manifested as a rapid, uncontrolled rise in blood pressure triggered by peripheral stimuli-a condition called autonomic dysreflexia. The objective was to demonstrate the translational potential of noninvasive transcutaneous stimulation (TCS) in mitigating autonomic dysreflexia following SCI, using pre-clinical evidence and a clinical case report. In rats with SCI, we show that TCS not only prevents the instigation of autonomic dysreflexia, but also mitigates its severity when delivered during an already-triggered episode. Furthermore, when TCS was delivered as a multisession therapy for 6 weeks post-SCI, the severity of autonomic dysreflexia was significantly reduced when tested in the absence of concurrent TCS. This treatment effect persisted for at least 1 week after the end of therapy. More importantly, we demonstrate the clinical applicability of TCS in treatment of autonomic dysreflexia in an individual with cervical, motor-complete, chronic SCI. We anticipate that TCS will offer significant therapeutic advantages, such as obviating the need for surgery resulting in reduced risk and medical expenses. Furthermore, this study provides a framework for testing the potential of TCS in improving recovery of other autonomic functions such lower urinary tract, bowel, and sexual dysfunction following SCI.

Vestibular Implant Imaging

Analogous to hearing restoration via cochlear implants, vestibular function could be restored via vestibular implants that electrically stimulate vestibular nerve branches to encode head motion. This study presents the technical feasibility and first imaging results of CT for vestibular implants in 8 participants of the first-in-human Multichannel Vestibular Implant Early Feasibility Study. Imaging characteristics of 8 participants (3 men, 5 women; median age, 59.5 years; range, 51-66 years) implanted with a Multichannel Vestibular Implant System who underwent a postimplantation multislice CT (n = 2) or flat panel CT (n = 6) are reported. The device comprises 9 platinum electrodes inserted into the ampullae of the 3 semicircular canals and 1 reference electrode inserted in the common crus. Electrode insertion site, positions, length and angle of insertion, and number of artifacts were assessed. Individual electrode contacts were barely discernible in the 2 participants imaged using multislice CT. Electrode and osseous structures were detectable but blurred so that only 12 of the 18 stimulating electrode contacts could be individually identified. Flat panel CT could identify all 10 electrode contacts in all 6 participants. The median reference electrode insertion depth angle was 9° (range, -57.5° to 45°), and the median reference electrode insertion length was 42 mm (range, -21-66 mm). Flat panel CT of vestibular implants produces higher-resolution images with fewer artifacts than multidetector row CT, allowing visualization of individual electrode contacts and quantification of their locations relative to vestibular semicircular canals and ampullae. As multichannel vestibular implant imaging improves, so will our understanding of the relationship between electrode placement and vestibular performance.

Microtopographical patterns promote different responses in fibroblasts and Schwann cells: A possible feature for neural implants

The chronic reliability of bioelectronic neural interfaces has been challenged by foreign body reactions (FBRs) resulting in fibrotic encapsulation and poor integration with neural tissue. Engineered microtopographies could alleviate these challenges by manipulating cellular responses to the implanted device. Parallel microchannels have been shown to modulate neuronal cell alignment and axonal growth, and Sharklet™ microtopographies of targeted feature sizes can modulate bio-adhesion of an array of bacteria, marine organisms, and epithelial cells due to their unique geometry. We hypothesized that a Sharklet™ micropattern could be identified that inhibited fibroblasts partially responsible for FBR while promoting Schwann cell proliferation and alignment. in vitro cell assays were used to screen the effect of Sharklet™ and channel micropatterns of varying dimensions from 2 to 20 μm on fibroblast and Schwann cell metrics (e.g., morphology/alignment, nuclei count, metabolic activity), and a hierarchical analysis of variance was used to compare treatments. In general, Schwann cells were found to be more metabolically active and aligned than fibroblasts when compared between the same pattern. 20 μm wide channels spaced 2 μm apart were found to promote Schwann cell attachment and alignment while simultaneously inhibiting fibroblasts and warrant further in vivo study on neural interface devices. No statistically significant trends between cellular responses and geometrical parameters were identified because mammalian cells can change their morphology dependent on their environment in a manner dissimilar to bacteria. Our results showed although surface patterning is a strong physical tool for modulating cell behavior, responses to micropatterns are highly dependent on the cell type.

1225-Channel Neuromorphic Retinal-Prosthesis SoC With Localized Temperature-Regulation

A 1225-Channel Neuromorphic Retinal Prosthesis (RP) SoC is presented. Existing RP SoCs directly convert light intensity to electrical stimulus, which limit the adoption of delicate stimulus patterns to increase visual acuity. Moreover, a conventional centralized image processor leads to the local hot spot that poses a risk to the nearby retinal cells. To solve these issues, the proposed SoC adopts a distributed Neuromorphic Image Processor (NMIP) located within each pixel that extracts the outline of the incoming image, which reduces current dispersion and stimulus power compared with light-intensity proportional stimulus pattern. A spike-based asynchronous digital operation results in the power consumption of 56.3 nW/Ch without local temperature hot spot. At every 5×5 pixels, the localized (49-point) temperature-regulation circuit limits the temperature increase of neighboring retinal cells to less than 1 °C, and the overall power consumption of the SoC to be less than that of the human eye. The 1225-channel SoC fabricated in 0.18 μm 1P6M CMOS occupies 15mm² while consuming 2.7 mW, and is successfully verified with image reconstruction demonstration.

Selective Activation of Cortical Columns Using Multichannel Magnetic Stimulation With a Bent Flat Microwire Array

OBJECTIVE

Cortical neural prostheses that aim to restore useful vision, hearing, and tactile sensations require the ability to selectively target different cortical regions simultaneously. Electrical stimulation via intracortical electrodes has been used to create spatial patterns of cortical activation. However, their efficacy remains limited due to the inability of conventional electrodes to confine activation to specific cortical regions around each electrode. Magnetic stimulation from single bent wires can selectively activate pyramidal neurons while avoiding passing axons, thereby confining activation to small cortical regions. This paper presents a novel bent flat microwire array and demonstrates its effectiveness for selective activation of cortical columns in mouse brain slices.

METHODS

A computational model was developed to compare the spatial resolution of magnetic stimulation from bent wire arrays with 280 and 530 μm tip spacings. The same array designs were fabricated for use in electrophysiological experiments, i.e., calcium imaging (GCaMP6s) of mouse brain slices.

RESULTS

All fabricated array designs reliably produced spatially discrete cortical activations at low stimulus amplitudes, but the 280-μm-spacing produced strong interference (constructive or destructive) at high stimulus amplitudes, thereby resulting in single strong activations or two asymmetric activations. 4-channel bent wire arrays with spacing of 340 μm avoided the interference and produced clearer spatial patterns of activation than electrodes.

CONCLUSION

Bent wire array designs can influence the strength or the spatial resolution of multichannel magnetic stimulation.

SIGNIFICANCE

These results suggest that bent microwire arrays can enhance the selectivity of multichannel stimulation of brain and therefore may help to develop reliable and effective cortical neural prostheses.

Predicting Response of Spontaneously Firing Afferents to Prosthetic Pulsatile Stimulation

Pulsatile electrical stimulation is used in neural prostheses such as the vestibular prosthesis. In a healthy vestibular system, head motion is encoded by changes in the firing rates of afferents around their spontaneous baseline rate. For people suffering from bilateral vestibular disorder (BVD), head motion no longer modulates firing rate. Vestibular prostheses use a gyroscope to detect head motion and stimulate neurons directly in a way that mimics natural modulation. Proper restoration of vestibular function relies on the ability of stimulation to evoke the same firing patterns as the healthy system. For this reason, it is necessary to understand what firing rates are produced for different stimulation parameters. Two stimulation parameters commonly controlled in pulsatile neuromodulation are pulse rate and pulse amplitude. Previous neural recording experiments in the vestibular nerve contradict widely held assumptions about the relationship between pulse rates and evoked spike activity, and the relationship between pulse amplitude and neural activity has not been explored. Here we use a well-established computational model of the vestibular afferent to simulate responses to different pulse rates and amplitudes. We confirm that our simulated neural results agree with the existing experimental data. Finally, we developed the "Action Potential Collision" (APC) equation that defines induced firing as a function of spontaneous firing rate, pulse rate, and pulse amplitude. We show that this relationship can successfully predict simulated vestibular activity by accounting for interactions between pulses and spontaneous firing.

Nanotunnels within Poly(3,4-ethylenedioxythiophene)-Carbon Nanotube Composite for Highly Sensitive Neural Interfacing

Neural electrodes are developed for direct communication with neural tissues for theranostics. Although various strategies have been employed to improve performance, creating an intimate electrode-tissue interface with high electrical fidelity remains a great challenge. Here, we report the rational design of a tunnel-like electrode coating comprising poly(3,4-ethylenedioxythiophene) (PEDOT) and carbon nanotubes (CNTs) for highly sensitive neural recording. The coated electrode shows a 50-fold reduction in electrochemical impedance at the biologically relevant frequency of 1 kHz, compared to the bare gold electrode. The incorporation of CNT significantly reinforces the nanotunnel structure and improves coating adhesion by ∼1.5 fold. In vitro primary neuron culture confirms an intimate contact between neurons and the PEDOT-CNT nanotunnel. During acute in vivo nerve recording, the coated electrode enables the capture of high-fidelity neural signals with low susceptibility to electrical noise and reveals the potential for precisely decoding sensory information through mechanical and thermal stimulation. These findings indicate that the PEDOT-CNT nanotunnel composite serves as an active interfacing material for neural electrodes, contributing to neural prosthesis and brain-machine interface.

Applying intuitive EEG-controlled grasp neuroprostheses in individuals with spinal cord injury: Preliminary results from the MoreGrasp clinical feasibility study

The aim of the MoreGrasp project is to develop a non-invasive, multimodal user interface including a brain-computer interface (BCI) for control of a grasp neuroprostheses in individuals with high spinal cord injury (SCI). The first results of the ongoing MoreGrasp clinical feasibility study involving end users with SCI are presented. This includes BCI screening sessions, in which we investigate the electroencephalography (EEG) patterns associated with single, natural movements of the upper limb. These patterns will later be used to control the neuroprosthesis. Additionally, the MoreGrasp grasp neuroprosthesis consisting of electrode arrays embedded in an individualized textile forearm sleeve is presented. The general feasibility of this electrode array in terms of corrections of misalignments during donning is shown together with the functional results in end users of the electrode forearm sleeve.

Zwitterionic Polymer Coating Suppresses Microglial Encapsulation to Neural Implants In Vitro and In Vivo

For brain computer interfaces (BCI), the immune response to implanted electrodes is a major biological cause of device failure. Bioactive coatings such as neural adhesion molecule L1 have been shown to improve the biocompatibility, but are difficult to handle or produce in batches. Here, a synthetic zwitterionic polymer coating, poly(sulfobetaine methacrylate) (PSBMA) is developed for neural implants with the goal of reducing the inflammatory host response. In tests in vitro, the zwitterionic coating inhibits protein adsorption and the attachment of fibroblasts and microglia, and remains stable for at least 4 weeks. In vivo two-photon microscopy on CX3CR1-GFP mice shows that the zwitterionic coating significantly suppresses the microglial encapsulation of neural microelectrodes over a 6 h observation period. Furthermore, the lower microglial encapsulation on zwitterionic polymer-coated microelectrodes is revealed to originate from a reduction in the size but not the number of microglial end feet. This work provides a facile method for coating neural implants with zwitterionic polymers and illustrates the initial interaction between microglia and coated surface at high temporal and spatial resolution.

Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images

Computational studies can be used to support the development of peripheral nerve interfaces, but currently use simplified models of nerve anatomy, which may impact the applicability of simulation results. To better quantify and model neural anatomy across the population, we have developed an algorithm to automatically reconstruct accurate peripheral nerve models from histological cross-sections. We acquired serial median nerve cross-sections from human cadaveric samples, staining one set with hematoxylin and eosin (H&E) and the other using immunohistochemistry (IHC) with anti-neurofilament antibody. We developed a four-step processing pipeline involving registration, fascicle detection, segmentation, and reconstruction. We compared the output of each step to manual ground truths, and additionally compared the final models to commonly used extrusions, via intersection-over-union (IOU). Fascicle detection and segmentation required the use of a neural network and active contours in H&E-stained images, but only simple image processing methods for IHC-stained images. Reconstruction achieved an IOU of 0.42±0.07 for H&E and 0.37±0.16 for IHC images, with errors partially attributable to global misalignment at the registration step, rather than poor reconstruction. This work provides a quantitative baseline for fully automatic construction of peripheral nerve models. Our models provided fascicular shape and branching information that would be lost via extrusion.

Restoring the Sense of Touch: From "Sci-Fi Dream" to Reality

A range of remarkable prostheses are now available to give function back to people who have had hands, arms, feet, or legs amputated, but for all their capabilities, these devices are missing a critical feature: a real sense of touch. Without it, a patient has no tactile sensory feedback on whether they have stepped off a curb or onto a misplaced child's toy, or are gripping a Styrofoam coffee cup or a toddler's hand too tightly or too loosely. Research projects today, however, are coming closer to restoring the sense of touch, which will help prostheses evolve from amazing tools to true replacement body parts.

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.

Combined optogenetic and electrical stimulation of auditory neurons increases effective stimulation frequency-an in vitro study

OBJECTIVE

The performance of neuroprostheses, including cochlear and retinal implants, is currently constrained by the spatial resolution of electrical stimulation. Optogenetics has improved the spatial control of neurons in vivo but lacks the fast-temporal dynamics required for auditory and retinal signalling. The objective of this study is to demonstrate that combining optical and electrical stimulation in vitro could address some of the limitations associated with each of the stimulus modes when used independently.

APPROACH

The response of murine auditory neurons expressing ChR2-H134 to combined optical and electrical stimulation was characterised using whole cell patch clamp electrophysiology.

MAIN RESULTS

Optogenetic costimulation produces a three-fold increase in peak firing rate compared to optical stimulation alone and allows spikes to be evoked by combined subthreshold optical and electrical inputs. Subthreshold optical depolarisation also facilitated spiking in auditory neurons for periods of up to 30 ms without evidence of wide-scale Na+ inactivation.

SIGNIFICANCE

These findings may contribute to the development of spatially and temporally selective optogenetic-based neuroprosthetics and complement recent developments in 'fast opsins'.

A Feasibility Study on Optically Transparent Encapsulation for Implantable Neural Prostheses

Optically transparent encapsulation is presented with results from long-term reliability and light transmission tests. This technology is required in certain implantable neural prostheses that demand the transmission of optical signals through an encapsulating material, such as in retinal implants or in optogenetic applications. In this study, biocompatible film-type cyclic olefin polymers (COPs) with low moisture absorption (<; 0.01 %) and high light transmission (92 %) are utilized as encapsulating materials based on thermal lamination. The reliability of COP encapsulation is characterized through accelerated soak tests in a 75 °C saline solution to measure the leakage currents from encapsulated inter-digitated electrodes. These tests had been done for 211 days with the estimated lifetime of 8.05 years at 37 °C. In addition, the optical properties of a thermally laminated COP film sample in relation to its thickness are evaluated by an experimental setup which uses projected line patterns on an image sensor. The light transmittance of COP film samples thinner than 376 μm exceeded 91.69 %, and the minimum distinguishable line pitch was 47.6 μm at a thickness of 26 μm. These results validate the feasibility of optically transparent encapsulation using COPs and may contribute to its use in future implantable neural prostheses.

Stretchability enhancement of electronics according to the surface patterns fabricated by thermal reflow process

Fabricating flexible and stretchable substrate for circuits is very important to make biomedical devices like biosensors or neural prosthesis. In this paper, the resistance changes of the polydimethylsiloxane (PDMS) film are observed when it is stretched. And the stretchability is measured and classified according to the shape of the engraved patterns. Patterns are fabricated on Si wafer by lithography. Thermal reflow process is conducted to modify the shape of the tips and we used spin coating method to make thin PDMS film. PDMS film is cut into uniform small pieces and stretched at a constant rate by the syringe pump. The resistance of the film is measured while film is stretched. According to the curvature and depth of the groove on the surface, the degree of the improvement varies. The deeper and round patterns enhance the stretchability of the film but sharp and shallow patterns degrade it.

Intraoperative Responses May Predict Chronic Performance of Composite Flat Interface Nerve Electrodes on Human Femoral Nerves

Peripheral nerve cuff electrodes (NCEs) in motor system neuroprostheses can generate strong muscle contractions and enhance surgical efficiency by accessing multiple muscles from a single proximal location. Predicting chronic performance of high contact density NCEs based on intraoperative observations would facilitate implantation at locations that maximize selective recruitment, immediate connection of optimal contacts to implanted pulse generators (IPGs) with limited output channels, and initiation of postoperative rehabilitation as soon as possible after surgery. However, the stability of NCE intraoperative recruitment to predict chronic performance has not been documented. Here we report the first-in-human application of a specific NCE, the composite flat interface nerve electrode (C-FINE), at a new and anatomically challenging location on the femoral nerve close to the inguinal ligaments. EMG and moment recruitment curves were recorded for each of the 8 contacts in 2 C-FINE intraoperatively, perioperatively, and chronically for 6 months. Intraoperative measurements predicted chronic outcomes for 87.5% of contacts with 14/16 recruiting the same muscles at 6 months as intraoperatively. In both 8-contact C-FINEs, 3 contacts elicited hip flexion and 5 selectively generated knee extension, 3 of which activated independent motor unit populations each sufficient to support standing. Recruitment order stabilized in less than 3 weeks and did not change thereafter. While confirmation of these results will be required with future studies and implant locations, this suggests that remobilization and stimulated exercise may be initiated 3 weeks after surgery with little risk of altering performance.

Zwitterionic polymer/polydopamine coating reduce acute inflammatory tissue responses to neural implants

The inflammatory brain tissue response to implanted neural electrode devices has hindered the longevity of these implants. Zwitterionic polymers have a potent anti-fouling effect that decreases the foreign body response to subcutaneous implants. In this study, we developed a nanoscale anti-fouling coating composed of zwitterionic poly (sulfobetaine methacrylate) (PSB) and polydopamine (PDA) for neural probes. The addition of PDA improved the stability of the coating compared to PSB alone, without compromising the anti-fouling properties of the film. PDA-PSB coating reduced protein adsorption by 89% compared to bare Si samples, while fibroblast adhesion was reduced by 86%. PDA-PSB coated silicon based neural probes were implanted into mouse brain, and the inflammatory tissue responses to the implants were assessed by immunohistochemistry one week after implantation. The PSB-PDA coated implants showed a significantly decreased expression of glial fibrillary acidic protein (GFAP), a marker for reactive astrocytes, within 70 μm from the electrode-tissue interface (p < 0.05). Additionally, the coating reduced the microglia activation as shown in decreased Iba-1 and lectin staining, and improved blood-brain barrier integrity indicated by reduced immunoglobulin (IgG) leakage into the tissue around the probes. These findings demonstrate that anti-fouling zwitterionic coating is effective in suppressing the acute inflammatory brain tissue response to implants, and should be further investigated for its potential to improve chronic performance of neural implants.

Aligned Conducting Polymer Nanotubes for Neural Prostheses

The long-term performance of neural microelectrodes relies on biocompatibility and sensitivity of the electrode-tissue interface. Current neural electrodes are limited by poor electrical performance including high initial impedance and low charge storage capacity. In addition, they are mechanically hard which causes cellular reactive response to the implanted electrode. In this report, we have demonstrated a new templating method for fabrication of highly aligned conducting polymer nanotube. The structure of nanotubes can be precisely modulated by varying the time of electropolymerization. The electrical performance of poly(pyrrole) (PPY) and poly(3,4-ethylenedioxythiophine) (PEDOT) nanotubes including impedance and charge storage capacity were studied and compared as the surface morphology and structure of nanotube varied during the fabrication process. PEDOT nanotubes were found to have lower impedance than PPY nanotubes. By contrast, PPY nanotubes were shown to have higher charge storage capacity. These finding suggest that aligned conducting polymer nanotubes may enhance the long-term performance of neural microelectrodes.

Computational Models And Tools For Developing Sophisticated Stimulation Strategies For Retinal Neuroprostheses

Improvements to the efficacy of retinal neuroprostheses can be achieved by developing more sophisticated neural stimulation strategies to enable selective or preferential activation of specific retinal ganglion cells (RGCs). Computational models are particularly well suited for these investigations. The electric field can be accurately described by mathematical formalisms, and the population-based neural responses to the electrical stimulation can be investigated at resolutions well beyond those achievable by current state-of-the-art biological techniques. In this study, we used a biophysically-and morphologically-detailed RGC model to explore the ability of high frequency electrical stimulation (HFS) to preferentially activate ON and OFF RGC subtypes, the two major information pathways of the retina. The performance of a wide range of electrical stimulation amplitudes (0 - $100~\mu \mathbf {A}$) and frequencies (1 - 10 kHz) on functionally-distinct RGC responses were evaluated. We found that ON RGCs could be preferentially activated at relatively higher stimulation amplitudes $( > 50 {\mu } \mathrm {A})$ and frequencies $( >2$ kHz) while OFF RGCs were activated by lower stimulation amplitudes (10 to $50 {\mu } \mathrm {A})$ across all tested frequencies. These stimuli also show great promise in eliciting RGC responses that parallel RGC encoding: one RGC type exhibited an increase in spiking activity during electrical stimulation whilst another exhibited decreased spiking activity, given the same stimulation parameters.

Multichannel Nerve Stimulation for Diverse Activation of Finger Flexors

OBJECTIVE

Neuromuscular electrical stimulation (NMES) is a common approach to restore muscle strength of individuals with a neurological injury but restoring hand dexterity is still a challenge. This study sought to quantify the diversity of finger movements elicited by a multichannel nerve stimulation technique.

METHODS

A 2 × 8 stimulation grid, placed on the upper arm along the ulnar and median nerves, was used to activate different finger flexors by automatically switching between randomized bipolar electrodes. The forces from each individual finger as well as the high-density electromyogram (HDEMG) of the intrinsic and extrinsic flexors were recorded. The elicited finger forces were categorized using hierarchical clustering, and the 2D correlation of the spatial patterns of muscle activation was also calculated.

RESULTS

A wide range of movement patterns were identified, including multi-finger and single-digit movements. Additionally, a number of electrode pairs elicited similar finger movements. The muscle activation patterns showed similar and distinct spatial patterns, signifying activation redundancy.

CONCLUSION

These results revealed the diversity of elicitable finger movements and muscle activations. The system redundancy can be explored to compensate for system instability due to fatigue or electrode shift. The outcomes can also enable the development of an automatic calibration of the stimulation.

Phase Synchronization Operator for On-Chip Brain Functional Connectivity Computation

This paper presents an integer-based digital processor for the calculation of phase synchronization between two neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters, and adders. The processor, fabricated in a 0.18- μm CMOS process, only occupies 0.05 mm ² and consumes 15 nW from a 0.5 V supply voltage at a signal input rate of 1024 S/s. These low-area and low-power features make the proposed processor a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for assessing the patterns of correlated activity in neural assemblies through the evaluation of functional connectivity maps.

A Robust Encoding Scheme for Delivering Artificial Sensory Information via Direct Brain Stimulation

Innovations for creating somatosensation via direct electrical stimulation of the brain will be required for the next generation of bi-directional cortical neuroprostheses. The current lack of tactile perception and proprioceptive input likely imposes a fundamental limit on speed and accuracy of brain-controlled prostheses or re-animated limbs. This study addresses the unique challenge of identifying a robust, high bandwidth sensory encoding scheme in a high-dimensional parameter space. Previous studies demonstrated single dimensional encoding schemes delivering low bandwidth sensory information, but no comparison has been performed across parameters, nor with update rates suitable for real-time operation of a neuroprosthesis. Here, we report the first comprehensive measurement of the resolution of key stimulation parameters such as pulse amplitude, pulse width, frequency, train interval and number of pulses. Surprisingly, modulation of stimulation frequency was largely undetectable. While we initially expected high frequency content to be an ideal candidate for passing high throughput sensory signals to the brain, we found only modulation of very low frequencies were detectable. Instead, the charge-per-phase of each pulse yields the highest resolution sensory signal, and is the key parameter modulating perceived intensity. The stimulation encoding patterns were designed for high-bandwidth information transfer that will be required for bi-directional brain interfaces. Our discovery of the stimulation features which best encode perceived intensity have significant implications for design of any neural interface seeking to convey information directly to the brain via electrical stimulation.

Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces

Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder's output, where the correction may over-dominate the user's intention. Reinforcement learning decoder allows users to actively adjust their brain patterns through trial and error, which better represents the subject's motive. The computational challenge is to quickly establish new state-action mapping before the subject becomes frustrated. Recently proposed quantized attention-gated kernel reinforcement learning (QAGKRL) explores the optimal nonlinear neural-action mapping in the Reproducing Kernel Hilbert Space (RKHS). However, considering all past data in RKHS is less efficient and sensitive to detect the new neural patterns emerging in brain control. In this paper, we propose a clustering-based kernel RL algorithm. New neural patterns emerge and are clustered to represent the novel knowledge in brain control. The current neural data only activate the nearest subspace in RKHS for more efficient decoding. The dynamic clustering makes our algorithm more sensitive to new brain patterns. We test our algorithm on both the synthetic and real-world spike data. Compared with QAGKRL, our algorithm can achieve a quicker knowledge adaptation in brain control with less computational complexity.

[Neuroprostheses to compensate for motor impairments]

Neuroprostheses are medical devices which, interfaced with the nervous system, are able to provoke the artificial generation of nerve signals. These signals, correctly coded, can then be interpreted by target organs such as the muscles.

Investigation of Injection Depth for Subretinal Delivery of Exogenous Glutamate to Restore Vision via Biomimetic Chemical Neuromodulation

Chemical neuromodulation of the retina using native neurotransmitters to biomimetically activate target retinal neurons through chemical synapses is a promising biomimetic alternative to electrical stimulation for restoring vision in blindness caused by photoreceptor degenerative diseases. Recent research has shown that subretinal chemical stimulation could be advantageous for treating photoreceptor degenerative diseases but many of the parameters for achieving efficacious chemical neuromodulation are yet to be explored. In this paper, we investigated how the depth at which neurotransmitter is injected subretinally affects the success rate, spike rate characteristics (i.e., amplitude, response latency, and time width), and spatial resolution of chemical stimulation in wild-type Long Evans and photoreceptor degenerated S334ter-3 transgenic rat retinas in vitro. We compared the responses to injections of glutamate at the subretinal surface and two subsurface depths near the outer and inner plexiform layers and found that while injections at all depths elicited robust retinal ganglion cell responses, they differed significantly in terms of the spike rate characteristics and spatial resolutions across injection depths. Shallow subsurface injections near the outer plexiform layer evoked the highest spike rate amplitudes and had the highest spatial resolution and success rates, while deep subsurface injections near the inner plexiform layer elicited the shortest latencies and narrowest time widths. Our results suggest that surface injections are suboptimal for subretinal chemical neuromodulation, while shallow subsurface and deep subsurface injections may optimize high spatial and high temporal resolution, respectively. These findings have great significance for the design and development of a potential neurotransmitter-based subretinal prosthesis.

Neuromodulation to improve gait and balance function using a sensory neuroprosthesis in people who report insensate feet - A randomized control cross-over study

Peripheral neuropathy may cause loss of sensory information from plantar cutaneous mechanoreceptors that is important for balance control and falls management. The current study investigated short-term effects of using Walkasins, an external lower-limb sensory neuroprosthesis, on clinical outcomes of balance and gait in persons who reported peripheral neuropathy and balance problems. The device replaces lost plantar sensation with tactile balance information that modulates cutaneous mechanoreceptors above the ankle where sensation is intact. Thirty-one male community-dwelling Veterans, 56–84 years old with insensate feet and balance problems participated. Initial Functional Gait Assessment, gait speed, and 4-Stage Balance Test outcomes were assessed. After initial assessment, subjects were randomly assigned to either wearing Walkasins turned ON, or OFF, and outcomes were re-assessed following a set of standardized balance exercises. Following a one-hour rest and washout period, treatments were crossed-over between groups and a third outcomes assessment was performed. Before cross-over, 10 of 15 subjects in the ON-then-OFF group improved their Functional Gait Assessment score by at least four points, the Minimal Clinically Important Difference, compared to 5 of 16 in the OFF-then-ON group. After cross-over, 7 of 16 subjects in the OFF-then-ON group improved by at least four points versus 2 of 15 in the ON-then-OFF group. ON treatment was associated with a Functional Gait Assessment improvement of 4.4 ± 3.7 points versus 1.5 ± 1.2 for the OFF treatment (p<0.01). Overall, Functional Gait Assessment scores changed from 15.2 ± 4.8 at initial assessment to 21.1 ± 5.2 after final assessment (p<0.001). At the end of the two treatment sessions, 16 of the 31 individuals had improved their Functional Gait Assessment score beyond 23, indicating normal fall-risk status. Future studies should investigate long-term benefits of the device to reduce fall risk and actual falls in patients with peripheral neuropathy and balance problems.

Precise Tubular Braid Structures of Ultrafine Microwires as Neural Probes: Significantly Reduced Chronic Immune Response and Greater Local Neural Survival in Rat Cortex

Braided multi-electrode probes (BMEPs) for neural interfaces comprise ultrafine microwire bundles interwoven into tubular braids. BMEPs provide highly flexible probes and tethers, and an open lattice structure with up to 24 recording/stimulating channels in precise geometries, currently all within a [Formula: see text] diameter footprint. This paper compares the long-term tissue effects of BMEPs ( [Formula: see text] wires) versus single conventional 50- [Formula: see text] wires, by testing nearby chronic immune response and neural survival in rat cortex. Four different types of electrodes were implanted in cortex in each of eight rats: 1) BMEP with tether; 2) tethered 50- [Formula: see text] wire; 3) BMEP without a tether; and 4) untethered 50- [Formula: see text] wire. Quantitative immunohistological statistical comparisons after eight weeks using GFAP, ED1, and NeuN staining clearly showed that both BMEP implants had significantly less tissue immune response and more neuronal survival than either of the 50- [Formula: see text] wires ( ) in each of the eight rats. Data strongly indicate that BMEP tissue responses are superior, and that BMEP designs partly alleviate chronic tissue inflammatory responses and neural losses. The flexible body, tether and open braid lattice, and finer wire diameters of BMEP designs may all contribute to reducing the biological long-term response.

Real-Time Performance of a Tactile Neuroprosthesis on Awake Behaving Rats

With the advancement of electrode and equipment technology, neuroprosthetics have become a promising alternative to partially compensate for the loss of sensorimotor function in amputees and patients with neurological diseases. Cortical neural interfaces are suitable especially for spinal cord injuries and amyotrophic lateral sclerosis. Although considerable success has been achieved in the literature by spike decoding of motor signals from the human brain, somatosensory feedback is essential for better motor control, interaction with objects, and the embodiment of prosthetic devices. In this paper, we present a tactile neuroprosthesis for rats based on intracortical microstimulation (ICMS). The rats wore mechanically-isolated boots covered with tactile sensors while performing a psychophysical detection task. The vibrotactile stimuli were measured by the artificial sensors and by using a real-time processor, this information was converted to electrical current pulses for ICMS. Some parameters of the real-time processor algorithm were specific to individual rats and were based on psychometric equivalence functions established earlier. Rats could detect the effects of the vibrotactile stimuli better (i.e., higher sensitivity indices) when the tactile neuroprosthesis was switched on compared to the boot only condition during active movement. In other words, the rats could decode the tactile information embedded in ICMS and use that in a behaviorally relevant manner. The presented animal model without peripheral nerve injury or amputation is also a promising tool to test various hardware and software components of neuroprosthetic systems in general.

Virtual Reality Provides an Effective Platform for Functional Evaluations of Closed-Loop Neuromyoelectric Control

Although recent advances in neuroprostheses offer opportunities for improved and intuitive control of advanced motorized and sensorized robotic arms, practical complications associated with such hardware can impede the research necessary for clinical translation. These hurdles potentially can be reduced with virtual reality environments (VREs) with embedded physics engines using virtual models of physical robotic hands. These software suites offer several advantages over physical prototypes, including high repeatability, reduced human error, elimination of many secondary sensory cues, and others. There are limited demonstrations of closed-loop prostheses in the VRE, and it is unclear whether VRE performance translates to the physical world. Here we describe how two trans-radial amputees with neural and intramuscular implants identified objects and performed activities of daily living with closed-loop control of prostheses in the VRE. Our initial evidence further suggests that capabilities with virtual prostheses may be predictors of physical prosthesis performance, demonstrating the utility of VREs for neuroprosthetic research.

Three-Dimensional Conductive Scaffolds as Neural Prostheses Based on Carbon Nanotubes and Polypyrrole

Three-dimensional scaffolds for cellular organization need to enjoy a series of specific properties. On the one hand, the morphology, shape and porosity are critical parameters and eventually related with the mechanical properties. On the other hand, electrical conductivity is an important asset when dealing with electroactive cells, so it is a desirable property even if the conductivity values are not particularly high. Here, we construct three-dimensional (3D) porous and conductive composites, where C8-D1A astrocytic cells were incubated to study their biocompatibility. The manufactured scaffolds are composed exclusively of carbon nanotubes (CNTs), a most promising material to interface with neuronal tissue, and polypyrrole (PPy), a conjugated polymer demonstrated to reduce gliosis, improve adaptability, and increase charge-transfer efficiency in brain-machine interfaces. We developed a new and easy strategy, based on the vapor phase polymerization (VPP) technique, where the monomer vapor is polymerized inside a sucrose sacrificial template containing CNT and an oxidizing agent. After removing the sucrose template, a 3D porous scaffold was obtained and its physical, chemical, and electrical properties were evaluated. The obtained scaffold showed very low density, high and homogeneous porosity, electrical conductivity, and Young's Modulus similar to the in vivo tissue. Its high biocompatibility was demonstrated even after 6 days of incubation, thus paving the way for the development of new conductive 3D scaffolds potentially useful in the field of electroactive tissues.

Neuroprostheses: method to evaluate the information content of stimulation strategies

We propose a framework to evaluate the information content of different stimulation strategies used in neuroprosthetic implants. We analyze the responses of retinal ganglion cells to electrical stimulation using an information theory framework. This methodology allows us to calculate the information content by looking at the consistency of neural responses generated across multiple repetitions of the same stimulation protocol.

Micro-Coil Design Influences the Spatial Extent of Responses to Intracortical Magnetic Stimulation

OBJECTIVE

Electrical stimulation via cortically implanted electrodes has been proposed to treat a wide range of neurological disorders. Effectiveness has been limited, however, in part due to the inability of conventional electrodes to activate specific types of neurons while avoiding other types. Recent demonstrations that magnetic stimulation from a micro-coil can selectively activate pyramidal neurons (PNs) while avoiding passing axons suggest the possibility that such an approach can overcome some this limitation and here we use computer simulations to explore how the micro-coil design influences the selectivity with which neurons are activated.

METHODS

A computational model was developed to compare the selectivity of magnetic stimulation induced by rectangular-, V-, and W-shaped coil designs. The more promising designs (V- and W-shapes) were fabricated for use in electrophysiological experiments including in vitro patch-clamp recording and calcium imaging (GCaMP6f) of mouse brain slices.

RESULTS

Both V- and W-shaped coils reliably activated layer 5 (L5) PNs but V-coils were more effective while W-coils were more selective. Activation thresholds with double-loop coils were approximately one-half those of single-loop coils. Calcium imaging revealed that both V- and W-coils better confine activation than electrodes.

CONCLUSION

Individual design features can influence both the strength as well as the selectivity of micro-coils and can be accurately predicted by computer simulations.

SIGNIFICANCE

Our results show that how coil design influences the response of cortical neurons to stimulation and are an important step toward the development of next-generation cortical prostheses.

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Single-Finger Neural Basis Information-Based Neural Decoder for Multi-Finger Movements

In this paper, we investigate the relationship between single and multi-finger movements. By exploiting the neural correlation between the temporal firing patterns between movements, we show that the Pearson's correlation coefficient for the physically related movement pairs are greater than those of others; the firing rates of the neurons that are tuned to a single-finger movements also increases when the corresponding multi-finger movements are instructed. We also use a hierarchical cluster analysis to verify not only the relationship between the single and multi-finger movements, but also the relationship between the flexion and extension movements. Furthermore, we propose a novel decoding method of modeling neural firing patterns while omitting the training process of the multi-finger movements. For the decoding, the Skellam and Gaussian probability distributions are used as mathematical models. The probabilistic distribution model of the multi-finger movements was estimated using the neural activity that was acquired during single-finger movements. As a result, the proposed neural decoding accuracy comparable with that of the supervised neural decoding accuracy when all of the neurons were used for the multi-finger movements. These results suggest that only the neural activities of single-finger movements can be exploited for the control of dexterous multi-finger neuroprosthetics.

Caricaturing faces to improve identity recognition in low vision simulations: How effective is current-generation automatic assignment of landmark points?

PURPOSE

Previous behavioural studies demonstrate that face caricaturing can provide an effective image enhancement method for improving poor face identity perception in low vision simulations (e.g., age-related macular degeneration, bionic eye). To translate caricaturing usefully to patients, assignment of the multiple face landmark points needed to produce the caricatures needs to be fully automatised. Recent development in computer science allows automatic face landmark detection of 68 points in real time and in multiple viewpoints. However, previous demonstrations of the behavioural effectiveness of caricaturing have used higher-precision caricatures with 147 landmark points per face, assigned by hand. Here, we test the effectiveness of the auto-assigned 68-point caricatures. We also compare this to the hand-assigned 147-point caricatures.

METHOD

We assessed human perception of how different in identity pairs of faces appear, when veridical (uncaricatured), caricatured with 68-points, and caricatured with 147-points. Across two experiments, we tested two types of low-vision images: a simulation of blur, as experienced in macular degeneration (testing two blur levels); and a simulation of the phosphenised images seen in prosthetic vision (at three resolutions).

RESULTS

The 68-point caricatures produced significant improvements in identity discrimination relative to veridical. They were approximately 50% as effective as the 147-point caricatures.

CONCLUSION

Realistic translation to patients (e.g., via real time caricaturing with the enhanced signal sent to smart glasses or visual prosthetic) is approaching feasibility. For maximum effectiveness software needs to be able to assign landmark points tracing out all details of feature and face shape, to produce high-precision caricatures.