Dynamic peripheral nerve stimulation can produce cortical activation similar to punctate mechanical stimuli.

During contact, phasic and tonic responses provide feedback that is used for task performance and perceptual processes. These disparate temporal dynamics are carried in peripheral nerves, and produce overlapping signals in cortex. Using longitudinal intrafascicular electrodes inserted into the median nerve of a nonhuman primate, we delivered composite stimulation consisting of onset and release bursts to capture rapidly adapting responses and sustained stochastic stimulation to capture the ongoing response of slowly adapting receptors. To measure the stimulation's effectiveness in producing natural responses, we monitored the local field potential in somatosensory cortex. We compared the cortical responses to peripheral nerve stimulation and vibrotactile/punctate stimulation of the fingertip, with particular focus on gamma band (30-65 Hz) responses. We found that vibrotactile stimulation produces consistently phase locked gamma throughout the duration of the stimulation. By contrast, punctate stimulation responses were phase locked at the onset and release of stimulation, but activity maintained through the stimulation was not phase locked. Using these responses as guideposts for assessing the response to the peripheral nerve stimulation, we found that constant frequency stimulation produced continual phase locking, whereas composite stimulation produced gamma enhancement throughout the stimulus, phase locked only at the onset and release of the stimulus. We describe this response as an "Appropriate Response in the gamma band" (ARγ), a trend seen in other sensory systems. Our demonstration is the first shown for intracortical somatosensory local field potentials. We argue that this stimulation paradigm produces a more biomimetic response in somatosensory cortex and is more likely to produce naturalistic sensations for readily usable neuroprosthetic feedback.

Prospective pre-operative 3-T MR neurography peripheral nerve mapping of upper extremity amputations implanted with FAST-LIFE electrode interfaces of robotic hands: technical report.

BACKGROUND AND PURPOSE: Fascicular targeting of longitudinal intrafascicular electrode (FAST-LIFE) interface enables hand dexterity with exogenous electrical microstimulation for sensory restoration, custom neural recording hardware, and deep learning-based artificial intelligence for motor intent decoding. The purpose of this technical report from a prospective pilot study was to illustrate magnetic resonance neurography (MRN) mapping of hand and nerve anatomy in amputees and incremental value of MRN over electrophysiology findings in pre-surgical planning of FAST-LIFE interface (robotic hand) patients. MATERIALS AND METHODS: After obtaining informed consent, patients with upper extremity amputations underwent pre-operative 3-T MRN, X-rays, and electrophysiology. MRN findings were correlated with electrophysiology reports. Descriptive statistics were performed. RESULTS: Five patients of ages 21-59 years exhibited 3/5 partial hand amputations, and 2/5 transradial amputations on X-rays. The median and ulnar nerve end bulb neuromas measured 10.1 ± 3.04 mm (range: 5.5-14 mm, median: 10.5 mm) and 10.9 ± 7.64 mm (2-22 mm, 9.75 mm), respectively. The ADC of median and ulnar nerves were increased at 1.64 ± 0.1 × 10-3 mm2/s (range: 1.5-1.8, median: 1.64 × 10-3 mm2/s) and 1.70 ± 0.17 × 10-3 mm2/s (1.49-1.98 × 10-3 mm2/s, 1.65 × 10-3 mm2/s), respectively. Other identified lesions were neuromas of superficial branch of the radial nerve and anterior interosseous nerve. On electrophysiology, 2/5 reports were unremarkable, 2/5 showed mixed motor-sensory neuropathies of median and ulnar nerves along with radial sensory neuropathy, and 1/5 showed sensory neuropathy of lateral cutaneous nerve of the forearm. All patients regained naturalistic sensations and motor control of digits. CONCLUSION: 3-T MRN allows excellent demonstration of forearm and hand nerve anatomy, altered diffusion characteristics, and their neuromas despite unremarkable electrophysiology for pre-surgical planning of the FAST-LIFE (robotic hand) interfaces.

Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface.

OBJECTIVE: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. METHODS: Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputee's movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder's performance is characterized in motor decoding experiments with three human amputees. RESULTS: First, we show the AI agent enables amputees to intuitively control a prosthetic hand with individual finger and wrist movements up to 97-98% accuracy. Second, we demonstrate the AI agent's real-time performance by measuring the reaction time and information throughput in a hand gesture matching task. Third, we investigate the AI agent's long-term uses and show the decoder's robust predictive performance over a 16-month implant duration. Conclusion & significance: Our study demonstrates the potential of AI-enabled nerve technology, underling the next generation of dexterous and intuitive prosthetic hands.

A portable, self-contained neuroprosthetic hand with deep learning-based finger control.

Objective.Deep learning-based neural decoders have emerged as the prominent approach to enable dexterous and intuitive control of neuroprosthetic hands. Yet few studies have materialized the use of deep learning in clinical settings due to its high computational requirements.Approach.Recent advancements of edge computing devices bring the potential to alleviate this problem. Here we present the implementation of a neuroprosthetic hand with embedded deep learning-based control. The neural decoder is designed based on the recurrent neural network architecture and deployed on the NVIDIA Jetson Nano-a compacted yet powerful edge computing platform for deep learning inference. This enables the implementation of the neuroprosthetic hand as a portable and self-contained unit with real-time control of individual finger movements.Main results.A pilot study with a transradial amputee is conducted to evaluate the proposed system using peripheral nerve signals acquired from implanted intrafascicular microelectrodes. The preliminary experiment results show the system's capabilities of providing robust, high-accuracy (95%-99%) and low-latency (50-120 ms) control of individual finger movements in various laboratory and real-world environments.Conclusion.This work is a technological demonstration of modern edge computing platforms to enable the effective use of deep learning-based neural decoders for neuroprosthesis control as an autonomous system.Significance.The proposed system helps pioneer the deployment of deep neural networks in clinical applications underlying a new class of wearable biomedical devices with embedded artificial intelligence.Clinical trial registration: DExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

Fascicle-Specific Targeting of Longitudinal Intrafascicular Electrodes for Motor and Sensory Restoration in Upper-Limb Amputees.

Multichannel longitudinal intrafascicular electrode (LIFE) interfaces provide optimized balance of invasiveness and stability for chronic sensory stimulation and motor recording/decoding of peripheral nerve signals. Using a fascicle-specific targeting (FAST)-LIFE approach, where electrodes are individually placed within discrete sensory- and motor-related fascicular subdivisions of the residual ulnar and/or median nerves in an amputated upper limb, FAST-LIFE interfacing can provide discernment of motor intent for individual digit control of a robotic hand, and restoration of touch- and movement-related sensory feedback. The authors describe their findings from clinical studies performed with 6 human amputee trials using FAST-LIFE interfacing of the residual upper limb.

Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals.

Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a "pseudo-online" dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.

A bioelectric neural interface towards intuitive prosthetic control for amputees.

Objective. While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful.Approach. Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject's motor intention.Main results. A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient's true intention.Significance. Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways.Clinical trial: DExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

Fascicle specific targeting for selective peripheral nerve stimulation.

OBJECTIVE: Electrical stimulation is a blunt tool for evoking neural activity. Neurons are naturally activated asynchronously and non-uniformly, whereas stimulation drives simultaneous activity within a population of cells. These differences in activation pattern can result in unintended side effects, including muddled sensory percepts and undesirable muscle contractions. These effects can be mitigated by the placement of electrodes in close approximation to nerve fibers and careful selection of the neural interface's location. This work describes the benefits of placing electrodes within specific fascicles of peripheral nerve to form selective neural interfaces for bidirectional neuroprosthetic devices. APPROACH: Chronic electrodes were targeted to individual fascicles of the ulnar and median nerves in the forearm of four human subjects. During the surgical implant procedure, fascicles were dissected from each nerve, and functional testing was used to identify the relative composition of sensory and motor fibers within each. FAST-LIFE arrays, composed of longitudinal intrafascicular arrays and fascicular cuff electrodes, were implanted in each fascicle. The location, quality, and stimulation parameters associated with sensations evoked by electrical stimulation on these electrodes were characterized throughout the 90-180 d implant period. MAIN RESULTS: FAST-LIFE arrays enable selective and chronic electrical stimulation of individual peripheral nerve fascicles. The quality of sensations evoked by stimulation in each fascicle is predictable and distinct; subjects reported tactile and cutaneous sensations during stimulation of sensory fascicles and deeper proprioceptive sensations during stimulation of motor fascicles. Stimulation thresholds and strength-duration time constants were typically higher within sensory fascicles. SIGNIFICANCE: Highly selective, stable neural interfaces can be created by placing electrodes within and around single fascicles of peripheral nerves. This method enables targeting electrodes to nerve fibers that innervate a specific body region or have specific functions. Fascicle-specific interfacing techniques have broad potential to maximize the therapeutic effects of electrical stimulation in many neuromodulation applications. (Clinical Trial ID NCT02994160.).

Human motor decoding from neural signals: a review.

Many people suffer from movement disability due to amputation or neurological diseases. Fortunately, with modern neurotechnology now it is possible to intercept motor control signals at various points along the neural transduction pathway and use that to drive external devices for communication or control. Here we will review the latest developments in human motor decoding. We reviewed the various strategies to decode motor intention from human and their respective advantages and challenges. Neural control signals can be intercepted at various points in the neural signal transduction pathway, including the brain (electroencephalography, electrocorticography, intracortical recordings), the nerves (peripheral nerve recordings) and the muscles (electromyography). We systematically discussed the sites of signal acquisition, available neural features, signal processing techniques and decoding algorithms in each of these potential interception points. Examples of applications and the current state-of-the-art performance were also reviewed. Although great strides have been made in human motor decoding, we are still far away from achieving naturalistic and dexterous control like our native limbs. Concerted efforts from material scientists, electrical engineers, and healthcare professionals are needed to further advance the field and make the technology widely available in clinical use.

Deep compressive autoencoder for action potential compression in large-scale neural recording.

OBJECTIVE: Understanding the coordinated activity underlying brain computations requires large-scale, simultaneous recordings from distributed neuronal structures at a cellular-level resolution. One major hurdle to design high-bandwidth, high-precision, large-scale neural interfaces lies in the formidable data streams (tens to hundreds of Gbps) that are generated by the recorder chip and need to be online transferred to a remote computer. The data rates can require hundreds to thousands of I/O pads on the recorder chip and power consumption on the order of Watts for data streaming alone. One of the solutions is to reduce the bandwidth of neural signals before transmission. APPROACH: We developed a deep learning-based compression model to reduce the data rate of multichannel action potentials. The proposed compression model is built upon a deep compressive autoencoder (CAE) with discrete latent embeddings. The encoder network of CAE is equipped with residual transformations to extract representative features from spikes, which are mapped into the latent embedding space and updated via vector quantization (VQ). The indexes of VQ codebook are further entropy coded as the compressed signals. The decoder network reconstructs spike waveforms with high quality from the quantized latent embeddings through stacked deconvolution. MAIN RESULTS: Extensive experimental results on both synthetic and in vivo datasets show that the proposed model consistently outperforms conventional methods that utilize hand-crafted features and/or signal-agnostic transformations and compressive sensing by achieving much higher compression ratios (20-500×) and better or comparable reconstruction accuracies. Testing results also indicate that CAE is robust against a diverse range of imperfections, such as waveform variation and spike misalignment, and has minor influence on spike sorting accuracy. Furthermore, we have estimated the hardware cost and real-time performance of CAE and shown that it could support thousands of recording channels simultaneously without excessive power/heat dissipation. SIGNIFICANCE: The proposed model can reduce the required data transmission bandwidth in large-scale recording experiments and maintain good signal qualities, which will be helpful to design power-efficient and lightweight wireless neural interfaces. We have open sourced the code implementation of the work at https://github.com/tong-wu-umn/spike-compression-autoencoder.

Asymmetric Sensory-Motor Regeneration of Transected Peripheral Nerves Using Molecular Guidance Cues.

Neural interfaces are designed to decode motor intent and evoke sensory precepts in amputees. In peripheral nerves, recording movement intent is challenging because motor axons are only a small fraction compared to sensory fibers and are heterogeneously mixed particularly at proximal levels. We previously reported that pain and myelinated axons regenerating through a Y-shaped nerve guide with sealed ends, can be modulated by luminar release of nerve growth factor (NGF) and neurotrophin-3 (NT-3), respectively. Here, we evaluate the differential potency of NGF, glial cell line-derived neurotrophic factor (GDNF), brain-derived neurotrophic factor (BDNF), pleiotrophin (PTN), and NT-3 in asymmetrically guiding the regeneration of sensory and motor neurons. We report that, in the absence of distal target organs, molecular guidance cues can mediate the growth of electrically conductive fascicles with normal microanatomy. Compared to Y-tube compartments with bovine serum albumin (BSA), GDNF and NGF increased the motor and sensory axon content, respectively. In addition, the sensory to motor ratio was significantly increased by PTN (12.7:1) when compared to a BDNF + GDNF choice. The differential content of motor and sensory axons modulated by selective guidance cues may provide a strategy to better define axon types in peripheral nerve interfaces.

An implantable, designed-for-human-use peripheral nerve stimulation and recording system for advanced prosthetics.

Complex suture prostheses that deliver sensory and position feedback require a more sophisticated integration with the human user. Here a micro-size active implantable system that provides many-degree-of-freedom neural feedback in both sensory stimulation and motor control is shown, as one potential human-use solution in DARPA's HAPTIX program. Various electrical and mechanical challenge and solutions in meeting both sensory /motor performance as well as ISO 14708 FDA-acceptable human use in an aspirin-size active implementation are discussed.

Detergent-free Decellularized Nerve Grafts for Long-gap Peripheral Nerve Reconstruction.

BACKGROUND: Long-gap peripheral nerve defects arising from tumor, trauma, or birth-related injuries requiring nerve reconstruction are currently treated using nerve autografts and nerve allografts. Autografts are associated with limited supply and donor-site morbidity. Allografts require administration of transient immunosuppressants, which has substantial associated risks. To overcome these limitations, we investigated the use of detergent-free decellularized nerve grafts to reconstruct long-gap nerve defects in a rodent model and compared it with existing detergent processing techniques. METHODS: Nerve grafts were harvested from the sciatic nerves of 9 donor rats. Twenty-four recipient rats were divided into 4 groups (6 animals per group): (1) nerve grafts (NG, positive control), (2) detergent-free decellularized (DFD) grafts, (3) detergent decellularized grafts, and (4) silicone tube conduits (negative control). Each recipient rat had a 3.5-cm graft or conduit sutured across a sciatic nerve transection injury. All animals were harvested at 12 weeks postimplantation for functional muscle analysis and nerve histomorphometry. RESULTS: Histomorphometry results indicated maximum growth in NG when compared with other groups. DFD and detergent decellularized groups showed comparable regeneration at 12 weeks. Silicone tube group showed no regeneration as expected. Muscle force data indicated functional recovery in NG and DFD groups only. CONCLUSIONS: This study describes a detergent-free nerve decellularization technique for reconstruction of long-gap nerve injuries. We compared DFD grafts with an established detergent processing technique and found that DFD nerve grafts are successful in promoting regeneration across long-gap peripheral nerve defects as an alternative to existing strategies.

On the robustness of EC-PC spike detection method for online neural recording.

BACKGROUND: Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. NEW METHOD: We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 µm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. RESULTS: Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. COMPARISON WITH EXISTING METHODS: Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. CONCLUSION: The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation.

Botulinum toxin suppression of CNS network activity in vitro.

The botulinum toxins are potent agents which disrupt synaptic transmission. While the standard method for BoNT detection and quantification is based on the mouse lethality assay, we have examined whether alterations in cultured neuronal network activity can be used to detect the functional effects of BoNT. Murine spinal cord and frontal cortex networks cultured on substrate integrated microelectrode arrays allowed monitoring of spontaneous spike and burst activity with exposure to BoNT serotype A (BoNT-A). Exposure to BoNT-A inhibited spike activity in cultured neuronal networks where, after a delay due to toxin internalization, the rate of activity loss depended on toxin concentration. Over a 30 hr exposure to BoNT-A, the minimum concentration detected was 2 ng/mL, a level consistent with mouse lethality studies. A small proportion of spinal cord networks, but not frontal cortex networks, showed a transient increase in spike and burst activity with exposure to BoNT-A, an effect likely due to preferential inhibition of inhibitory synapses expressed in this tissue. Lastly, prior exposure to human-derived antisera containing neutralizing antibodies prevented BoNT-A induced inhibition of network spike activity. These observations suggest that the extracellular recording from cultured neuronal networks can be used to detect and quantify functional BoNT effects.

Thiol-ene/acrylate substrates for softening intracortical electrodes.

Neural interfaces have traditionally been fabricated on rigid and planar substrates, including silicon and engineering thermoplastics. However, the neural tissue with which these devices interact is both 3D and highly compliant. The mechanical mismatch at the biotic-abiotic interface is expected to contribute to the tissue response that limits chronic signal recording and stimulation. In this work, novel ternary thiol-ene/acrylate polymer networks are used to create softening substrates for neural recording electrodes. Thermomechanical properties of the substrates are studied through differential scanning calorimetry and dynamic mechanical analysis both before and after exposure physiological conditions. This substrate system softens from more than 1 GPa to 18 MPa on exposure to physiological conditions: reaching body temperature and taking up less than 3% fluid. The impedance of 177 µm(2) gold electrodes electroplated with platinum black fabricated on these substrates is measured to be 206 kΩ at 1 kHz. Specifically, intracortical electrodes are fabricated, implanted, and used to record driven neural activity. This work describes the first substrate system that can use the full capabilities of photolithography, respond to physiological conditions by softening markedly after insertion, and record driven neural activity for 4 weeks.

Effects of carbon nanotube and conducting polymer coated microelectrodes on single-unit recordings in vitro.

Neuronal networks cultured on microelectrode arrays (MEAs) have been utilized as biosensors that can detect all or nothing extracellular action potentials, or spikes. Coating the microelectrodes with carbon nanotubes (CNTs), either pristine or conjugated with a conductive polymer, has been previously reported to improve extracellular recordings presumably via reduction in microelectrode impedance. The goal of this work was to examine the basis of such improvement in vitro. Every other microelectrode of in vitro MEAs was electrochemically modified with either conducting polymer, poly-3,4-ethylenedioxythiophene (PEDOT) or a blend of CNT and PEDOT. Mouse cortical tissue was dissociated and cultured on the MEAs to form functional neuronal networks. The performance of the modified and unmodified microelectrodes was evaluated by activity measures such as spike rate, spike amplitude, burst duration and burst rate. We observed that the yield, defined as percentage of microelectrodes with neuronal activity, was significantly higher by 55% for modified microelectrodes compared to the unmodified sites. However, the spike rate and burst parameters were similar for modified and unmodified microelectrodes suggesting that neuronal networks were not physiologically altered by presence of PEDOT or PEDOT-CNT. Our observations from immunocytochemistry indicated that neuronal cells were more abundant in proximity to modified microelectrodes.

Normal molecular repair mechanisms in regenerative peripheral nerve interfaces allow recording of early spike activity despite immature myelination.

Clinical use of neurally controlled prosthetics has advanced in recent years, but limitations still remain, including lacking fine motor control and sensory feedback. Indwelling multi-electrode arrays, cuff electrodes, and regenerative sieve electrodes have been reported to serve as peripheral neural interfaces, though long-term stability of the nerve-electrode interface has remained a formidable challenge. We recently developed a regenerative multi-electrode interface (REMI) that is able to record neural activity as early as seven days post-implantation. While this activity might represent normal neural depolarization during axonal regrowth, it can also be the result of altered nerve regeneration around the REMI. This study evaluated high-throughput expression levels of 84 genes involved in nerve injury and repair, and the histological changes that occur in parallel to this early neural activity. Animals exhibiting spike activity increased from 29% to 57% from 7 to 14 days following REMI implantation with a corresponding increase in firing rate of 113%. Two weeks after implantation, numbers of neurofilament-positive axons in the control and REMI implanted nerves were comparable, and in both cases the number of myelinated axons was low. During this time, expression levels of genes related to nerve injury and repair were similar in regenerated nerves, both in the presence or absence of the electrode array. Together, these results indicate that the early neural activity is intrinsic to the regenerating axons, and not induced by the REMI neurointerface.

The use of a novel carbon nanotube coated microelectrode array for chronic intracortical recording and microstimulation.

Micro-electrode arrays (MEAs) have been used in a variety of intracortical neural prostheses. While intracortical MEAs have demonstrated their utility in neural prostheses, in many cases MEA performance declines after several months to years of in vivo implantation. The application of carbon nanotubes (CNTs) may increase the functional longevity of intracortical MEAs through enhanced biocompatibility and charge injection properties. An MEA metalized with platinum (Pt) on all electrodes had a CNT coating applied to the electrodes on half of the array. This Pt/Pt-CNT MEA was implanted into feline motor cortex for >1 year. Recordings of action potentials and 1 kHz impedance measurements were made on all electrodes to evaluate device functionality. Additionally, electromyogram (EMG) responses were evoked using micro-stimulation via the MEA to measure device performance. These metrics were compared between Pt and Pt-CNT electrodes. There was no significant difference in the data acquisition or micro-stimulation performance of Pt and the Pt-CNT electrodes. However, impedances were lower on the Pt-CNT electrodes. These results demonstrate the functionality of CNT coatings during chronic in vivo implantation. The lower impedances suggest that for microstimulation applications CNT coatings may impart enhanced interface properties.

1/f neural noise reduction and spike feature extraction using a subset of informative samples.

This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.