Multi-contact Nerve Research Articles

Case Series of Multicontact Peripheral Nerve Stimulation in the Transversus Abdominis Plane for Chronic Abdominal Wall Pain

ObjectivesManaging abdominal pain can be difficult. This is due to the nonspecific nature of the pain, the multiple etiologies, and the different mechanisms underlying this type of pain. Abdominal wall pain in particular poses its own challenges. Traditionally, chronic abdominal wall pain has been managed with nonopioid analgesics, and in severe cases, opioid therapy has been considered. For patients with chronic abdominal wall pain refractory to medication management, peripheral nerve blocks and spinal cord stimulation also have been trialed with some success. In this study, we present a case series in patients with chronic abdominal wall pain who were treated with a multicontact peripheral nerve stimulation (PNS) system in the transversus abdominis plane (TAP). Materials and MethodsThis was a single-center, retrospective case series. Data were included from adults with chronic abdominal wall pain whose symptoms were refractory to standard medical management and who underwent a multicontact PNS system placement in the TAP. ResultsFour patients met the inclusion criteria. All four patients underwent a multicontact PNS trial lead placement in the TAP. One patient reported no benefit from the trial. The remaining three patients underwent a permanent multicontact PNS system placement in the TAP. ConclusionsIn patients with chronic abdominal wall pain whose symptoms are refractory to conservative medical management, PNS may be an alternative treatment option. As the use of PNS for chronic abdominal wall pain and other fascial planes continues to develop, additional research is necessary to determine optimal placements and specific stimulation parameters.

Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.

Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrimination of activity from different neural pathways recorded by a high-density multi-contact nerve cuff electrode, achieving state-of-the-art performance but requiring too much data storage and power for a practical implementation on surgically implanted hardware. To reduce resource utilization for an implantable implementation, with minimal performance loss for CNNs that can discriminate between neural pathways in multi-contact cuff electrode recordings. Neural networks (NNs) were evaluated using rat sciatic nerve recordings previously collected using 56-channel cuff electrodes to capture spatiotemporal neural activity patterns. NNs were trained to classify individual, natural compound action potentials (nCAPs) elicited by sensory stimuli. Three architectures were explored: the previously reported ESCAPE-NET, a fully convolutional network, and a recurrent neural network. Variations of each architecture were evaluated based on F1-score, number of weights, and floating-point operations (FLOPs). NNs were identified that, when compared to ESCAPE-NET, require 1,132-1,787x fewer weights, 389-995x less memory, and 6-11,073x fewer FLOPs, while maintaining macro F1-scores of 0.70-0.71 compared to a baseline of 0.75. Memory requirements range from 22.69 KB to 58.11 KB, falling within on-chip memory sizes from published deep learning accelerators fabricated in ASIC technology. Reduced versions of ESCAPE-NET require significantly fewer resources without significant accuracy loss, thus can be more easily incorporated into a surgically implantable device that performs closed-loop responsive neural stimulation.

Interleaved Multi-Contact Peripheral Nerve Stimulation to Enhance Reproduction of Tactile Sensation: A Computational Modeling Study.

Peripheral nerve stimulation (PNS) is an effective means to elicit sensation for rehabilitation of people with loss of a limb or limb function. While most current PNS paradigms deliver current through single electrode contacts to elicit each tactile percept, multi-contact extraneural electrodes offer the opportunity to deliver PNS with groups of contacts individually or simultaneously. Multi-contact PNS strategies could be advantageous in developing biomimetic PNS paradigms to recreate the natural neural activity during touch, because they may be able to selectively recruit multiple distinct neural populations. We used computational models and optimization approaches to develop a novel biomimetic PNS paradigm that uses interleaved multi-contact (IMC) PNS to approximate the critical neural coding properties underlying touch. The IMC paradigm combines field shaping, in which two contacts are active simultaneously, with pulse-by-pulse contact and parameter variations throughout the touch stimulus. We show in simulation that IMC PNS results in better neural code mimicry than single contact PNS created with the same optimization techniques, and that field steering via two-contact IMC PNS results in better neural code mimicry than one-contact IMC PNS. We also show that IMC PNS results in better neural code mimicry than existing PNS paradigms, including prior biomimetic PNS. Future clinical studies will determine if the IMC paradigm can improve the naturalness and usefulness of sensory feedback for those with neurological disorders.

Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface.

Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. The objective of this study was to demonstrate the feasibility of this approach in the context of closed-loop stimulation. Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel ( 8×8 ) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was successfully demonstrated in 6 subjects. The number of successful movement sequence trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.

Plantar somatosensory restoration enhances gait, speed perception, and motor adaptation.

Lower limb loss is a major insult to the body's nervous and musculoskeletal systems. Despite technological advances in prosthesis design, artificial limbs are not yet integrated into the body's physiological systems. Therefore, lower limb amputees (LLAs) experience lower balance confidence, higher fear of falls, and impaired gait compared with their able-bodied peers (ABs). Previous studies have demonstrated that restored sensations perceived as originating directly from the missing limb via neural interfaces improve balance and performance in certain ambulatory tasks; however, the effects of such evoked sensations on neural circuitries involved in the locomotor activity are not well understood. In this work, we investigated the effects of plantar sensation elicited by peripheral nerve stimulation delivered by multicontact nerve cuff electrodes on gait symmetry and stability, speed perception, and motor adaptation. We found that restored plantar sensation increased stance time and propulsive force on the prosthetic side, improved gait symmetry, and yielded an enhanced perception of prosthetic limb movement. Our results show that the locomotor adaptation among LLAs with plantar sensation became similar to that of ABs. These findings suggest that our peripheral nerve-based approach to elicit plantar sensation directly affects central nervous pathways involved in locomotion and motor adaptation during walking. Our neuroprosthesis provided a unique model to investigate the role of somatosensation in the lower limb during walking and its effects on perceptual recalibration after a locomotor adaptation task. Furthermore, we demonstrated how plantar sensation in LLAs could effectively increase mobility, improve walking dynamics, and possibly reduce fall risks.

Fully Customizable, Low-Cost, Multi-Contact Nerve Cuffs for Spatially Selective Neuromodulation.

Selective neuromodulation of peripheral nerves is an emerging treatment for neurological diseases that are resistant to traditional drug therapy. While nerve cuffs with multichannel stimulation can be made by many varied methods, they usually require specialized microfabrication or additive manufacturing equipment. A truly low-cost and effective method of creating a custom cuff has not been accessible to researchers to prototype new methodologies and therapies in acute studies. Here, we present an inexpensive, highly repeatable method to create multi-contact nerve cuffs that require a simple postproduction PEDOT:PSS coating to improve the tissue/electrode interface. We demonstrate spatially selective neuromodulation with the proposed cuff design on the rat sciatic by preferentially activating the tibialis anterior (TA) and the lateral gastrocnemius (LG) in longitudinal and transverse stimulation patterns. This demonstrates that the proposed cuff fabrication method was not only effective for selective neuromodulation, but it is also significantly lower in cost, fully-customizable, and easily manufactured for future selective neuromodulation studies.

The Fifth Bioelectronic Medicine Summit Hosted by the Feinstein Institutes for Medical Research (Manhasset, NY) and Columbia Engineering (NY, NY) at The Garden City Hotel, Garden City, NY 11530 on October 11-12th, 2022- Bioelectronic Medicine: Today’s Tools, Tomorrow’s Therapies

The Fifth Bioelectronic Medicine Summit Hosted by the Feinstein Institutes for Medical Research (Manhasset, NY) and Columbia Engineering (NY, NY) at The Garden City Hotel, Garden City, NY 11530 on October 11-12th, 2022- Bioelectronic Medicine: Today’s Tools, Tomorrow’s Therapies

Selective neural stimulation methods improve cycling exercise performance after spinal cord injury: a case series

BackgroundExercise after paralysis can help prevent secondary health complications, but achieving adequate exercise volumes and intensities is difficult with loss of motor control. Existing electrical stimulation-driven cycling systems involve the paralyzed musculature but result in rapid force decline and muscle fatigue, limiting their effectiveness. This study explores the effects of selective stimulation patterns delivered through multi-contact nerve cuff electrodes on functional exercise output, with the goal of increasing work performed and power maintained within each bout of exercise.MethodsThree people with spinal cord injury and implanted stimulation systems performed cycling trials using conventional (S-Max), low overlap (S-Low), low duty cycle (C-Max), and/or combined low overlap and low duty cycle (C-Low) stimulation patterns. Outcome measures include total work (W), end power (Pend), power fluctuation indices (PFI), charge accumulation (Q), and efficiency (η). Mann–Whitney tests were used for statistical comparisons of W and Pend between a selective pattern and S-Max. Welch’s ANOVAs were used to evaluate differences in PFIs among all patterns tested within a participant (n ≥ 90 per stimulation condition).ResultsAt least one selective pattern significantly (p < 0.05) increased W and Pend over S-Max in each participant. All selective patterns also reduced Q and increased η compared with S-Max for all participants. C-Max significantly (p < 0.01) increased PFI, indicating a decrease in ride smoothness with low duty cycle patterns.ConclusionsSelective stimulation patterns can increase work performed and power sustained by paralyzed muscles prior to fatigue with increased stimulation efficiency. While still effective, low duty cycle patterns can cause inconsistent power outputs each pedal stroke, but this can be managed by utilizing optimized stimulation levels. Increasing work and sustained power each exercise session has the potential to ultimately improve the physiological benefits of stimulation-driven exercise.

Selective stimulation of the ferret abdominal vagus nerve with multi-contact nerve cuff electrodes

Dysfunction and diseases of the gastrointestinal (GI) tract are a major driver of medical care. The vagus nerve innervates and controls multiple organs of the GI tract and vagus nerve stimulation (VNS) could provide a means for affecting GI function and treating disease. However, the vagus nerve also innervates many other organs throughout the body, and off-target effects of VNS could cause major side effects such as changes in blood pressure. In this study, we aimed to achieve selective stimulation of populations of vagal afferents using a multi-contact cuff electrode wrapped around the abdominal trunks of the vagus nerve. Four-contact nerve cuff electrodes were implanted around the dorsal (N = 3) or ventral (N = 3) abdominal vagus nerve in six ferrets, and the response to stimulation was measured via a 32-channel microelectrode array (MEA) inserted into the left or right nodose ganglion. Selectivity was characterized by the ability to evoke responses in MEA channels through one bipolar pair of cuff contacts but not through the other bipolar pair. We demonstrated that it was possible to selectively activate subpopulations of vagal neurons using abdominal VNS. Additionally, we quantified the conduction velocity of evoked responses to determine what types of nerve fibers (i.e., Aδ vs. C) responded to stimulation. We also quantified the spatial organization of evoked responses in the nodose MEA to determine if there is somatotopic organization of the neurons in that ganglion. Finally, we demonstrated in a separate set of three ferrets that stimulation of the abdominal vagus via a four-contact cuff could selectively alter gastric myoelectric activity, suggesting that abdominal VNS can potentially be used to control GI function.

Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study.

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm could maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.

Implanted High Density Cuff Electrodes Functionally Activate Human Tibial and Peroneal Motor Units Without Chronic Detriment to Peripheral Nerve Health

Peripheral nerve stimulation via multi-contact nerve cuff electrodes (NCEs) has proved effective in restoring function to individuals with lower-extremity paralysis. This study investigates clinical measures of nerve health over one year post-implantation of a composite flat-interface nerve electrode (C-FINE) on the tibial and peroneal nerves above the knee in a human volunteer. This represents the first deployment of a novel NCE on new neural targets in a uniquely challenging location prone to prolonged externally applied forces, making acute and chronic postoperative observation critical. A 27-year-old man with an incomplete spinal cord injury (AIS C) at the C3 to C4 level received eight-contact C-FINEs bilaterally on the tibial and peroneal nerves, proximal to the knee. Access to four contacts per cuff exhibiting the most desirable responses was externalized via temporary percutaneous leads. Percutaneous leads were later removed, with contacts generating the best dorsiflexion (two of four) and plantar flexion (one of four) reconnected to a permanently implanted pulse generator. For 13 months post-implantation, nerve health and cuff performance were assessed through motor nerve conduction velocity (MNCV) studies, clinical needle electromyography, compound motor action potential (CMAP), sensory nerve action potential (SNAP), stimulation-evoked tetanic moment collection, and lower-limb circumference measurements. Tibial and peroneal MNCVs remained stable bilaterally above 40 m/sec, with CMAPs increased or stable after six months. SNAPs remained stable across all measurements. CMAP initial charge thresholds remained below 50 nC, with minimal changes to muscle recruitment order in three of four externalized contacts per cuff. Peak tetanic moments remained stable, with bilateral increases in thigh and calf circumferences of 5% and 14% over one year. Above-knee tibial and peroneal NCEs can restore stimulated ankle-joint function without chronic nerve health detriments. Alongside previous femoral nerve data, this study demonstrates the ability of NCEs to enhance lower-extremity function with limited neuromuscular impact.

Impact of Encapsulation Tissue Growth on Selective Recording in Nerve Cuff Electrodes: A Simulation Study.

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation systems. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. We investigated the performance of a CNN-based selective recording approach in the presence of encapsulation tissue, a common immune response to the implantation of a neural interface. This factor was simulated using anatomically accurate computational models of a rat sciatic nerve and nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised retraining with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. The periodic recalibration approach demonstrated the best results, with an average F1-score of 0.96 ± 0.04, 0.89 ± 0.08, and 0.80 ± 0.08 for SNRs of -5 dB, -10 dB, and -15 dB, respectively, across all time points. Thus, the periodic recalibration approach may be an effective solution to compensate for changes in signal recordings seen over time as a result of encapsulation tissue. The self-learning approach, in which a network is retrained periodically using predicted labels, generally showed degradation in classification performance over time, even as the frequency of training was increased, attributed to an eventual accumulation of mislabeled training data.

Selective peripheral nerve recordings from nerve cuff electrodes using convolutional neural networks

Objective. Recording and stimulating from the peripheral nervous system are becoming important components in a new generation of bioelectronics systems. Although neurostimulation has seen a history of successful chronic applications in humans, peripheral nerve recording in humans chronically remains a challenge. Multi-contact nerve cuff electrode configurations have the potential to improve recording selectivity. We introduce the idea of using a convolutional neural network (CNN) to associate recordings of individual naturally evoked compound action potentials (CAPs) with neural pathways of interest, by exploiting the spatiotemporal patterns in multi-contact nerve cuff recordings. Approach. Nine Long-Evan rats were implanted with a 56-channel nerve cuff electrode on the sciatic nerve and afferent activity was selectively evoked in different fascicles (tibial, peroneal, sural) using mechanical stimuli. A recurrent neural network was then used to predict joint angles based on the predicted firing patterns from the CNN. Performance was measured based on the classification accuracy, F1-score and the ability to track the ankle joint angle. Main results. Classification accuracy and F1-score of the best CNN configuration were and 0.747 ± 0.114, respectively. The mean Pearson correlation coefficient between the manually measured ankle angle and the angle predicted from the estimated firing rate was Significance. The proposed method demonstrates that CAP-based classification can be achieved with high accuracy and can be used to track a physiological meaningful measure (e.g. joint angle). These results provide a promising direction for realizing more effective and intuitive neuroprosthetic systems.

Classification of naturally evoked compound action potentials in peripheral nerve spatiotemporal recordings

Peripheral neural signals have the potential to provide the necessary motor, sensory or autonomic information for robust control in many neuroprosthetic and neuromodulation applications. However, developing methods to recover information encoded in these signals is a significant challenge. We introduce the idea of using spatiotemporal signatures extracted from multi-contact nerve cuff electrode recordings to classify naturally evoked compound action potentials (CAP). 9 Long-Evan rats were implanted with a 56-channel nerve cuff on the sciatic nerve. Afferent activity was selectively evoked in the different fascicles of the sciatic nerve (tibial, peroneal, sural) using mechano-sensory stimuli. Spatiotemporal signatures of recorded CAPs were used to train three different classifiers. Performance was measured based on the classification accuracy, F1-score, and the ability to reconstruct original firing rates of neural pathways. The mean classification accuracies, for a 3-class problem, for the best performing classifier was 0.686 ± 0.126 and corresponding mean F1-score was 0.605 ± 0.212. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates found for the best classifier was 0.728 ± 0.276. The proposed method demonstrates the possibility of classifying individual naturally evoked CAPs in peripheral neural signals recorded from extraneural electrodes, allowing for more precise control signals in neuroprosthetic applications.

Evaluation of high-density, multi-contact nerve cuffs for activation of grasp muscles in monkeys

Objective. The objective of this work was to evaluate whether nerve cuffs can selectively activate hand muscles for functional electrical stimulation (FES). FES typically involves identifying and implanting electrodes in many individual muscles, but nerve cuffs only require implantation at a single site around the nerve. This method is surgically more attractive. Nerve cuffs may also more effectively stimulate intrinsic hand muscles, which are difficult to implant and stimulate without spillover to adjacent muscles. Approach. To evaluate its ability to selectively activate muscles, we implanted and tested the flat interface nerve electrode (FINE), which is designed to selectively stimulate peripheral nerves that innervate multiple muscles (Tyler and Durand 2002 IEEE Trans. Neural Syst. Rehabil. Eng. 10 294–303). We implanted FINEs on the nerves and bipolar intramuscular wires for recording compound muscle action potentials (CMAPs) from up to 20 muscles in each arm of six monkeys. We then collected recruitment curves while the animals were anesthetized. Main result. A single FINE implanted on an upper extremity nerve in the monkey can selectively activate muscles or small groups of muscles to produce multiple, independent hand functions. Significance. FINE cuffs can serve as a viable supplement to intramuscular electrodes in FES systems, where they can better activate intrinsic and extrinsic muscles with lower currents and less extensive surgery.

Use of spatiotemporal templates for pathway discrimination in peripheral nerve recordings: a simulation study

Objective. Extraction of information from the peripheral nervous system can provide control signals in neuroprosthetic applications. However, the ability to selectively record from different pathways within peripheral nerves is limited. We investigated the integration of spatial and temporal information for pathway discrimination in peripheral nerves using measurements from a multi-contact nerve cuff electrode. Approach. Spatiotemporal templates were established for different neural pathways of interest, and used to obtain tailored matched filters for each of these pathways. Simulated measurements of compound action potentials propagating through the nerve in different test cases were used to evaluate classification accuracy, percentage of missed spikes, and ability to reconstruct the original firing rates of the neural pathways. Main results. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates over all tests cases was found to be 0.832 ± 0.161, 0.421 ± 0.145, 0.481 ± 0.340 for our algorithm, Bayesian spatial filters, and velocity selective recordings respectively. Significance. The proposed method shows that the spatiotemporal templates were able to provide more robust spike detection and reliable pathway discrimination than these existing algorithms.

Influence of Anatomical Detail and Tissue Conductivity Variations in Simulations of Multi-Contact Nerve Cuff Recordings.

Accurate simulations of peripheral nerve recordings are needed to develop improved neuroprostheses. Previous models of peripheral nerves contained simplifications whose effects have not been investigated. We created a novel detailed finite element (FE) model of a peripheral nerve, and used it to carry out a sensitivity analysis of several model parameters. To construct the model, in vivo recordings were obtained in a rat sciatic nerve using an 8-channel nerve cuff electrode, after which the nerve was imaged using magnetic resonance imaging (MRI). The FE model was constructed based on the MRI data, and included progressive branching of the fascicles. Neural pathways were defined in the model for the tibial, peroneal and sural fascicles. The locations of these pathways were selected so as to maximize the correlations between the simulated and in vivo recordings. The sensitivity analysis showed that varying the conductivities of neural tissues had little influence on the ability of the model to reproduce the recording patterns obtained experimentally. On the other hand, the increased anatomical detail did substantially alter the recording patterns observed, demonstrating that incorporating fascicular branching is an important consideration in models of nerve cuff recordings. The model used in this study constitutes an improved simulation tool and can be used in the design of neural interfaces.

Fabrication of High Contact-Density, Flat-Interface Nerve Electrodes for Recording and Stimulation Applications.

Many attempts have been made to manufacture multi-contact nerve cuff electrodes that are safe, robust and reliable for long term neuroprosthetic applications. This protocol describes a fabrication technique of a modified cylindrical nerve cuff electrode to meet these criteria. Minimum computer-aided design and manufacturing (CAD and CAM) skills are necessary to consistently produce cuffs with high precision (contact placement 0.51 ± 0.04 mm) and various cuff sizes. The precision in spatially distributing the contacts and the ability to retain a predefined geometry accomplished with this design are two criteria essential to optimize the cuff's interface for selective recording and stimulation. The presented design also maximizes the flexibility in the longitudinal direction while maintaining sufficient rigidity in the transverse direction to reshape the nerve by using materials with different elasticities. The expansion of the cuff's cross sectional area as a result of increasing the pressure inside the cuff was observed to be 25% at 67 mm Hg. This test demonstrates the flexibility of the cuff and its response to nerve swelling post-implant. The stability of the contacts' interface and recording quality were also examined with contacts' impedance and signal-to-noise ratio metrics from a chronically implanted cuff (7.5 months), and observed to be 2.55 ± 0.25 kΩ and 5.10 ± 0.81 dB respectively.

Effects of the choice of reference on the selectivity of a multi-contact nerve cuff electrode.

Tripolar referencing is typically used in nerve cuff electrode recordings due to its ability to maximize the signal-to-noise ratio of contacts at the centre, but this may not be the optimal choice for a multi-contact nerve cuff consisting of contacts in off-centre rings. We conducted a simulation study to compare the effects of 3 different reference types on the recording selectivity of a multi-contact nerve cuff: the tripolar reference (TPR), common average reference (CAR), and multiple tripolar references based on consecutive groups of 3 rings (cTPR). For this purpose, we introduce a novel measure called the contact information metric (CIM). Selectivity was tested in 2 noise settings, one in which white Gaussian noise was added inside the nerve cuff electrode and the other in which electromyogram (EMG) noise was added outside the nerve cuff electrode. The mean CIMs values calculated for the best 8 contacts were 3.42±6.25, 2.70±3.37, and 3.65±1.90 for the TPR, the CAR and the cTPR, respectively, in the case of EMG noise added outside the nerve cuff electrode. This study shows that the use of cTPR reference is the optimal choice for selectivity when using a multi-contact nerve cuff electrode which contains off-centre rings.

Optimization of selective stimulation parameters for multi-contact electrodes

BackgroundMulti-contact stimulating electrodes are gaining acceptance as a means for interfacing with the peripheral nervous system. These electrodes can potentially activate many independent populations of motor units within a single peripheral nerve, but quantifying their recruitment properties and the overlap in stimulation between contacts is difficult and time consuming. Further, current methods for quantifying overlap between contacts are ambiguous and can lead to suboptimal selective stimulation parameters. This study describes a novel method for optimizing stimulation parameters for multi-contact peripheral stimulating electrodes to produce strong, selective muscle contractions. The method is tested with four-contact spiral nerve-cuff electrodes implanted on bilateral femoral nerves of two individuals with spinal cord injury, but it is designed to be extendable to other electrode technologies with higher densities of contacts.MethodsTo optimize selective stimulation parameters for multi-contact electrodes, first, recruitment and overlap are characterized for all contacts within an electrode. Recruitment is measured with the twitch response to single stimulus pulses, and overlap between pairs of contacts is quantified by the deviation in their combined response from linear addition of individual responses. Simple mathematical models are fit to recruitment and overlap data, and a cost function is defined to maximize recruitment and minimize overlap between all contacts.ResultsResults are presented for four-contact nerve-cuff electrodes stimulating bilateral femoral nerves of two human subjects with spinal cord injury. Knee extension moments between 11.6 and 43.2 Nm were achieved with selective stimulation through multiple contacts of each nerve-cuff with less than 10% overlap between pairs of contacts. The overlap in stimulation measured in response to selective stimulation parameters was stable at multiple repeated time points after implantation.ConclusionsThese results suggest that the method described here can provide an automated means of determining stimulus parameters to achieve strong muscle contractions via selective stimulation through multi-contact peripheral nerve electrodes.