Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

Mathematical definition and rules of the splitting/merging patterns in bundles of human peripheral nerve segment

Accurately measuring the spatial extension distance of nerve bundles in completing a split/merge is impossible because no clear mathematical definition exists for the starting and ending positions in nerve-bundle splitting/merging. We manually count the number of nerve-bundle splits/merges in long nerve segments, which is labor-intensive, inefficient, and prone to counting errors. Currently, the mathematics are unclear for the nerve-bundle diameter before and after splitting/merging. This paper explores these problems and proposes nerve-bundle splitting/merging rules. Based on the method of defining the beginning and ending positions of nerve-bundle splitting/merging, we explored the mathematical law of equivalent diameter of nerve bundles before and after splitting/merging. The experimental results revealed that the moving average of circularity of nerve bundle accurately defines the beginning and ending positions of nerve-bundle splitting/merging. The diameter of the nerve bundles before and after split/merge approximately conforms to the principles of the Da Vinci formula. The proposed automatic counting algorithm based on centroid offset matching obtains the number of split/merged nerve bundles in the sequence scan images with 100 % accuracy. The mathematical definition of the starting and ending positions of nerve-bundle splitting/merging proposed in this paper is accurate and strict and is the foundation of subsequent research. The proposed automatic counting algorithm based on centroid offset matching (ACA-COM) can accurately and efficiently count the number of times the nerve bundles split and merge in sequential images. The mathematical law satisfied by the diameter of the nerve bundles before and after splitting/merging reflects that the nerve bundles tend to have better capability to resist breaking.

Accelerating neurotechnology development using an Agile methodology

Novel bioelectronic medical devices that target neural control of visceral organs (e.g., liver, gut, spleen) or inflammatory reflex pathways are innovative class III medical devices like implantable cardiac pacemakers that are lifesaving and life-sustaining medical devices. Bringing innovative neurotechnologies early into the market and the hands of treatment providers would benefit a large population of patients inflicted with autonomic and chronic immune disorders. Medical device manufacturers and software developers widely use the Waterfall methodology to implement design controls through verification and validation. In the Waterfall methodology, after identifying user needs, a functional unit is fabricated following the verification loop (design, build, and verify) and then validated against user needs. Considerable time can lapse in building, verifying, and validating the product because this methodology has limitations for adjusting to unanticipated changes. The time lost in device development can cause significant delays in final production, increase costs, and may even result in the abandonment of the device development. Software developers have successfully implemented an Agile methodology that overcomes these limitations in developing medical software. However, Agile methodology is not routinely used to develop medical devices with implantable hardware because of the increased regulatory burden of the need to conduct animal and human studies. Here, we provide the pros and cons of the Waterfall methodology and make a case for adopting the Agile methodology in developing medical devices with physical components. We utilize a peripheral nerve interface as an example device to illustrate the use of the Agile approach to develop neurotechnologies.

Merging Humans and Neuroprosthetics through Regenerative Peripheral Nerve Interfaces

Limb amputations can be devastating and significantly affect an individual's independence, leading to functional and psychosocial challenges in nearly 2 million people in the United States alone. Over the past decade, robotic devices driven by neural signals such as neuroprostheses have shown great potential to restore the lost function of limbs, allowing amputees to regain movement and sensation. However, current neuroprosthetic interfaces have challenges in both signal quality and long-term stability. To overcome these limitations and work toward creating bionic limbs, the Neuromuscular Laboratory at University of Michigan Plastic Surgery has developed the Regenerative Peripheral Nerve Interface (RPNI). This surgical construct embeds a transected peripheral nerve into a free muscle graft, effectively amplifying small peripheral nerve signals to provide enhanced control signals for a neuroprosthetic limb. Furthermore, the RPNI has the potential to provide sensory feedback to the user and facilitate neuroprosthesis embodiment. This review focuses on the animal studies and clinical trials of the RPNI to recapitulate the promising trajectory toward neurobionics where the boundary between an artificial device and the human body becomes indistinct. This paper also sheds light on the prospects of the improvement and dissemination of the RPNI technology.

Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review

Peripheral nerve interfaces (PNIs) are electrical systems designed to integrate with peripheral nerves in patients, such as following central nervous system (CNS) injuries to augment or replace CNS control and restore function. We review the literature for clinical trials and studies containing clinical outcome measures to explore the utility of human applications of PNIs. We discuss the various types of electrodes currently used for PNI systems and their functionalities and limitations. We discuss important design characteristics of PNI systems, including biocompatibility, resolution and specificity, efficacy, and longevity, to highlight their importance in the current and future development of PNIs. The clinical outcomes of PNI systems are also discussed. Finally, we review relevant PNI clinical trials that were conducted, up to the present date, to restore the sensory and motor function of upper or lower limbs in amputees, spinal cord injury patients, or intact individuals and describe their significant findings. This review highlights the current progress in the field of PNIs and serves as a foundation for future development and application of PNI systems.

Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks

BACKGROUND

It is difficult to create intuitive methods of controlling prosthetic limbs, often resulting in abandonment. Peripheral nerve interfaces can be used to convert motor intent into commands to a prosthesis. The Extraneural Spatiotemporal Compound Action Potentials Extraction Network (ESCAPE-NET) is a convolutional neural network (CNN) that has previously been demonstrated to be effective at discriminating neural sources in rat sciatic nerves. ESCAPE-NET was designed to operate using data from multi-channel nerve cuff arrays, and use the resulting spatiotemporal signatures to classify individual naturally evoked compound action potentials (nCAPs) based on differing source fascicles. The applicability of this approach to larger and more complex nerves is not well understood. To support future translation to humans, the objective of this study was to characterize the performance of this approach in a computational model of the human median nerve.

METHODS

Using a cross-sectional immunohistochemistry image of a human median nerve, a finite-element model was generated and used to simulate extraneural recordings. ESCAPE-NET was used to classify nCAPs based on source location, for varying numbers of sources and noise levels. The performance of ESCAPE-NET was also compared to ResNet-50 and MobileNet-V2 in the context of classifying human nerve cuff data.

RESULTS

Classification accuracy was found to be inversely related to the number of nCAP sources in ESCAPE-NET (3-class: 97.8% ± 0.1%; 10-class: 89.3% ± 5.4% in low-noise conditions, 3-class: 70.3% ± 0.1%; 10-class: 52.5% ± 0.3% in high-noise conditions). ESCAPE-NET overall outperformed both MobileNet-V2 (3-class: 96.5% ± 1.1%; 10-class: 84.9% ± 1.7% in low-noise conditions, 3-class: 86.0% ± 0.6%; 10-class: 41.4% ± 0.9% in high-noise conditions) and ResNet-50 (3-class: 71.2% ± 18.6%; 10-class: 40.1% ± 22.5% in low-noise conditions, 3-class: 81.3% ± 4.4%; 10-class: 31.9% ± 4.4% in high-noise conditions).

CONCLUSION

All three networks were found to learn to differentiate nCAPs from different sources, as evidenced by performance levels well above chance in all cases. ESCAPE-NET was found to have the most robust performance, despite decreasing performance as the number of classes increased, and as noise was varied. These results provide valuable translational guidelines for designing neural interfaces for human use.

Vibrotactile Feedback for a Person with Transradial Amputation and Visual Loss: A Case Report

Background and Objectives: After major upper-limb amputation, people face challenges due to losing tactile information and gripping function in their hands. While vision can confirm the success of an action, relying on it diverts attention from other sensations and tasks. This case report presents a 30-year-old man with traumatic, complete vision loss and transradial left forearm amputation. It emphasizes the importance of restoring tactile abilities when visual compensation is impossible. Materials and Methods: A prototype tactile feedback add-on system was developed, consisting of a sensor glove and upper arm cuff with related vibration actuators. Results: We found a 66% improvement in the Box and Blocks test and an overall functional score increase from 30% to 43% in the Southampton Hand Assessment Procedure with feedback. Qualitative improvements in bimanual activities, ergonomics, and reduced reliance on the unaffected hand were observed. Incorporating the tactile feedback system improved the precision of grasping and the utility of the myoelectric hand prosthesis, freeing the unaffected hand for other tasks. Conclusions: This case demonstrated improvements in prosthetic hand utility achieved by restoring peripheral sensitivity while excluding the possibility of visual compensation. Restoring tactile information from the hand and fingers could benefit individuals with impaired vision and somatosensation, improving acceptance, embodiment, social integration, and pain management.

Detection and discrimination of electrical stimuli from an upper limb cuff electrode inM. Mulatta

Objective.Peripheral nerve interfaces seek to restore nervous system function through electrical stimulation of peripheral nerves. In clinical use, these devices should function reliably for years or decades. In this study, we assessed evoked sensations from multi-channel cuff electrode stimulation in macaque monkeys up to 711 d post-implantation.Approach.Three trained macaque monkeys received multi-channel cuff electrode implants at the median or ulnar nerves in the upper arm. Electrical stimuli from the cuff interfaces evoked sensations, which we measured via standard psychophysical tasks. We adjusted pulse amplitude or pulse width for each block with various electrode channel configurations to examine the effects of stimulus parameterization on sensation. We measured detection thresholds and just-noticeable differences (JNDs) at irregular, near-daily intervals for several months using Bayesian inferencing from trial data. We examined data trends using classical models such as Weber's Law and the strength-duration relationship using linear regression.Main results.Detection thresholds were similar between blocks with pulse width modulation and blocks with pulse amplitude modulation when represented as charge per pulse, the product of the amplitude and the pulse width. Conversely, Weber fractions-calculated as the slope of the regression between JND charge values and reference stimulus charge-were significantly different between pulse width and pulse amplitude modulation blocks for the discrimination task.Significance.Weber fractions were lower in blocks with amplitude modulation than in blocks with pulse width modulation, suggesting that pulse amplitude modulation allows finer resolution of sensory encoding above threshold. Consequently, amplitude modulation may enable a greater dynamic range for sensory perception with neuroprosthetic devices.

Characterizing sensory thresholds and intensity sensitivity of Regenerative Peripheral Nerve Interfaces: A Case Study. IEEE Int Conf Rehabil Robot 2022;2022:1-6. [PMID: 36176116 DOI: 10.1109/icorr55369.2022.9896481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Current prosthetic limbs offer little to no sensory feedback. Developments in peripheral nerve interfaces provide opportunities to restore some level of tactile feedback that is referred to the prosthetic limb. One such method is a Regenerative Peripheral Nerve Interface (RPNI), composed of a muscle graft wrapped around a free nerve ending. Here, we characterize perception and discomfort thresholds, as well as sensitivity to stimulation through two-alternative forced choice discrimination tasks. One person with transradial amputation who had one RPNI constructed from the median nerve and two constructed from the ulnar nerve participated. Average perception thresholds across all RPNIs were between 950 and 1120 nC with variance of less than 350 nC over a 36-month period. Discomfort thresholds were from 3880 nC to 9770 nC across all RPNIs. The just noticeable difference for the Median RPNI was 520 nC, larger than either the Ulnar-1 or Ulnar-2 RPNIs (210 nC, 470 nC, respectively). We also calculated Weber fractions to compare sensitivity between different RPNIs and relate our results to previous studies. Weber fractions for each of the Median, Ulnar-1, and Ulnar-2 RPNIs were 0.134, 0.088, 0.087, respectively. This work is the first to quantify the functional stimulation range and sensitivity of RPNIs in a human participant. Future work will focus on characterizing RPNI sensation in additional individuals to determine if these findings are generalizable to the amputee population.

Electrotactile Feedback Applications for Hand and Arm Interactions: A Systematic Review, Meta-Analysis, and Future Directions

Haptic feedback is critical in a broad range of human-machine/computer-interaction applications. However, the high cost and low portability/wearability of haptic devices remain unresolved issues, severely limiting the adoption of this otherwise promising technology. Electrotactile interfaces have the advantage of being more portable and wearable due to their reduced actuators' size, as well as their lower power consumption and manufacturing cost. The applications of electrotactile feedback have been explored in human-computer interaction and human-machine-interaction for facilitating hand-based interactions in applications, such as prosthetics, virtual reality, robotic teleoperation, surface haptics, portable devices, and rehabilitation. This article presents a technological overview of electrotactile feedback, as well a systematic review and meta-analysis of its applications for hand-based interactions. We discuss the different electrotactile systems according to the type of application. We also discuss over a quantitative congregation of the findings, to offer a high-level overview into the state-of-art and suggest future directions. Electrotactile feedback systems showed increased portability/wearability, and they were successful in rendering and/or augmenting most tactile sensations, eliciting perceptual processes, and improving performance in many scenarios. However, knowledge gaps (e.g., embodiment), technical (e.g., recurrent calibration, electrodes' durability) and methodological (e.g., sample size) drawbacks were detected, which should be addressed in future studies.

Clinical Basis for Creating an Osseointegrated Neural Interface

As technology continues to improve within the neuroprosthetic landscape, there has been a paradigm shift in the approach to amputation and surgical implementation of haptic neural prosthesis for limb restoration. The Osseointegrated Neural Interface (ONI) is a proposed solution involving the transposition of terminal nerves into the medullary canal of long bones. This design combines concepts of neuroma formation and prevention with osseointegration to provide a stable environment for conduction of neural signals for sophisticated prosthetic control. While this concept has previously been explored in animal models, it has yet to be explored in humans. This anatomic study used three upper limb and three lower limb cadavers to assess the clinical feasibility of creating an ONI in humans. Anatomical measurement of the major peripheral nerves- circumference, length, and depth- were performed as they are critical for electrode design and rerouting of the nerves into the long bones. CT imaging was used for morphologic bone evaluation and virtual implantation of two osseointegrated implants were performed to assess the amount of residual medullary space available for housing the neural interfacing hardware. Use of a small stem osseointegrated implant was found to reduce bone removal and provide more intramedullary space than a traditional implant; however, the higher the amputation site, the less medullary space was available regardless of implant type. Thus the stability of the endoprosthesis must be maximized while still maintaining enough residual space for the interface components. The results from this study provide an anatomic basis required for establishing a clinically applicable ONI in humans. They may serve as a guide for surgical implementation of an osseointegrated endoprosthesis with intramedullary electrodes for prosthetic control.

Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics

Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

Whole Brain Hemodynamic Response Based on Synchrony Analysis of Brain Signals for Effective Application of HD-tDCS in Stroke Patients: An fNIRS Study

In this study, the effective application of high-definition transcranial direct current stimulation (HD-tDCS) based on the whole brain hemodynamic response in stroke patients was investigated using functional near-infrared spectroscopy (fNIRS). The intrahemispheric and interhemispheric synchronization and cortical activity based on the time during 1 mA HD-tDCS were examined in 26 chronic cerebrovascular disease patients. At the beginning of HD-tDCS, the synchronization and brain activity in the whole brain increased rapidly and decreased after 5 min. In the middle of tDCS, the synchronization began to increase again, and strong synchronic connections were formed around the desired stimulation area. After tDCS, strong cortical activation was observed in the stimulation area, indicating that the baseline of the oxyhemoglobin (HbO) signal increased in the desired stimulation area. Therefore, the results of this study indicate that HD-tDCS can be applied efficiently to enhance the effect of tDCS. This stimulation method with tDCS can be explored clinically for more neurorehabilitation of patients with degenerative brain diseases.

Phrenic nerve paralysis and phrenic nerve reconstruction surgery

Phrenic nerve injury results in paralysis of the diaphragm muscle, the primary generator of an inspiratory effort, as well as a stabilizing muscle involved in postural control and spinal alignment. Unilateral deficits often result in exertional dyspnea, orthopnea, and sleep-disordered breathing, whereas oxygen or ventilator dependency can occur with bilateral paralysis. Common etiologies of phrenic injuries include cervical trauma, iatrogenic injury in the neck or chest, and neuralgic amyotrophy. Many patients have no identifiable etiology and are considered to have idiopathic paralysis. Diagnostic evaluation requires radiographic and pulmonary function testing, as well as electrodiagnostic assessment to quantitate the nerve deficit and determine the extent of denervation atrophy. Treatment for symptomatic diaphragm paralysis has traditionally been limited. Medical therapies and nocturnal positive airway pressure may provide some benefit. Surgical repair of the nerve injury to restore functional diaphragmatic activity, termed phrenic nerve reconstruction, is a safe and effective alternative to static repositioning of the diaphragm (diaphragm plication), in properly selected patients. Phrenic nerve reconstruction has increasingly become a standard surgical treatment for diaphragm paralysis due to phrenic nerve injury. A multidisciplinary approach at specialty referral centers combining diagnostic evaluation, surgical treatment, and rehabilitation is required to achieve optimal long-term outcomes.

Three dimensional microelectrodes enable high signal and spatial resolution for neural seizure recordings in brain slices and freely behaving animals

Neural recordings made to date through various approaches—both in-vitro or in-vivo—lack high spatial resolution and a high signal-to-noise ratio (SNR) required for detailed understanding of brain function, synaptic plasticity, and dysfunction. These shortcomings in turn deter the ability to further design diagnostic, therapeutic strategies and the fabrication of neuro-modulatory devices with various feedback loop systems. We report here on the simulation and fabrication of fully configurable neural micro-electrodes that can be used for both in vitro and in vivo applications, with three-dimensional semi-insulated structures patterned onto custom, fine-pitch, high density arrays. These microelectrodes were interfaced with isolated brain slices as well as implanted in brains of freely behaving rats to demonstrate their ability to maintain a high SNR. Moreover, the electrodes enabled the detection of epileptiform events and high frequency oscillations in an epilepsy model thus offering a diagnostic potential for neurological disorders such as epilepsy. These microelectrodes provide unique opportunities to study brain activity under normal and various pathological conditions, both in-vivo and in in-vitro, thus furthering the ability to develop drug screening and neuromodulation systems that could accurately record and map the activity of large neural networks over an extended time period.

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.

Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI

One of the primary objectives of the brain-computer interface (BCI) is to obtain a command with higher classification accuracy within the shortest possible time duration. Therefore, this study evaluates several stimulation durations to propose a duration that can yield the highest classification accuracy. Furthermore, this study aims to address the inherent delay in the hemodynamic responses (HRs) for the command generation time. To this end, HRs in the sensorimotor cortex were evaluated for the functional near-infrared spectroscopy (fNIRS)-based BCI. To evoke brain activity, right-hand-index finger poking and tapping tasks were used. In this study, six different stimulation durations (i.e., 1, 3, 5, 7, 10, and 15 s) were tested on 10 healthy male subjects. Upon stimulation, different temporal features and multiple time windows were utilized to extract temporal features. The extracted features were then classified using linear discriminant analysis. The classification results using the main HR showed that a 5 s stimulation duration could yield the highest classification accuracy, i.e., 74%, with a combination of the mean and maximum value features. However, the results were not significantly different from the classification accuracy obtained using the 15 s stimulation. To further validate the results, a classification using the initial dip was performed. The results obtained endorsed the finding with an average classification accuracy of 73.5% using the features of minimum peak and skewness in the 5 s window. The results based on classification using the initial dip for 5 s were significantly different from all other tested stimulation durations (p < 0.05) for all feature combinations. Moreover, from the visual inspection of the HRs, it is observed that the initial dip occurred as soon as the task started, but the main HR had a delay of more than 2 s. Another interesting finding is that impulsive stimulation in the sensorimotor cortex can result in the generation of a clearer initial dip phenomenon. The results reveal that the command for the fNIRS-based BCI can be generated using the 5 s stimulation duration. In conclusion, the use of the initial dip can reduce the time taken for the generation of commands and can be used to achieve a higher classification accuracy for the fNIRS-BCI within a 5 s task duration rather than relying on longer durations.

Decoding attempted phantom hand movements from ipsilateral sensorimotor areas after amputation

Objective.The sensorimotor cortex is often selected as target in the development of a Brain-Computer Interface, as activation patterns from this region can be robustly decoded to discriminate between different movements the user executes. Up until recently, such BCIs were primarily based on activity in the contralateral hemisphere, where decoding movements still works even years after denervation. However, there is increasing evidence for a role of the sensorimotor cortex in controlling the ipsilateral body. The aim of this study is to investigate the effects of denervation on the movement representation on the ipsilateral sensorimotor cortex.Approach.Eight subjects with acquired above-elbow arm amputation and nine controls performed a task in which they made (or attempted to make with their phantom hand) six different gestures from the American Manual Alphabet. Brain activity was measured using 7T functional MRI, and a classifier was trained to discriminate between activation patterns on four different regions of interest (ROIs) on the ipsilateral sensorimotor cortex.Main results.Classification scores showed that decoding was possible and significantly better than chance level for both the phantom and intact hands from all ROIs. Decoding both the left (intact) and right (phantom) hand from the same hemisphere was also possible with above-chance level classification score.Significance.The possibility to decode both hands from the same hemisphere, even years after denervation, indicates that implantation of motor-electrodes for BCI control possibly need only cover a single hemisphere, making surgery less invasive, and increasing options for people with lateralized damage to motor cortex like after stroke.

The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements

Over the last few decades there has been a push to enhance the use of advanced prosthetics within the fields of biomedical engineering, neuroscience, and surgery. Through the development of peripheral neural interfaces and invasive electrodes, an individual's own nervous system can be used to control a prosthesis. With novel improvements in neural recording and signal decoding, this intimate communication has paved the way for bidirectional and intuitive control of prostheses. While various collaborations between engineers and surgeons have led to considerable success with motor control and pain management, it has been significantly more challenging to restore sensation. Many of the existing peripheral neural interfaces have demonstrated success in one of these modalities; however, none are currently able to fully restore limb function. Though this is in part due to the complexity of the human somatosensory system and stability of bioelectronics, the fragmentary and as-yet uncoordinated nature of the neuroprosthetic industry further complicates this advancement. In this review, we provide a comprehensive overview of the current field of neuroprosthetics and explore potential strategies to address its unique challenges. These include exploration of electrodes, surgical techniques, control methods, and prosthetic technology. Additionally, we propose a new approach to optimizing prosthetic limb function and facilitating clinical application by capitalizing on available resources. It is incumbent upon academia and industry to encourage collaboration and utilization of different peripheral neural interfaces in combination with each other to create versatile limbs that not only improve function but quality of life. Despite the rapidly evolving technology, if the field continues to work in divided "silos," we will delay achieving the critical, valuable outcome: creating a prosthetic limb that is right for the patient and positively affects their life.

Fabrication of titanium dioxide nanomaterial for implantable highly flexible composite bioelectrode for biosensing applications

Implantable and stretchable electrodes have managed to progress the medical field from a medical diagnosis aspect to a patient treatment level. They offer the ability to detect biosignals and conduct electrical current to tissues that aid in muscle stimulation and axon regeneration. Current conventional electrodes are fabricated from stiff and very expensive, precious metals such as platinum. In this work, novel, low cost, and highly flexible electrode materials were fabricated based on titanium dioxide (TiO₂) and polymethyl methacrylate (PMMA) supported by a silicone polymer matrix. The electrode materials were characterized by their electrochemical, mechanical, and surface properties. The electrodes possessed high flexibility with Young's modulus of 235 kPa, revealing highly stretchable characteristics. The impedance at 1 kHz was around 114.6 kΩ, and the charge capacity was 1.23 mC/cm². The fabricated electrodes appeared to have a smooth surface, as seen in the scanning electron microscope micrographs, compared with electrodes in the literature. Long-time stability tests revealed an overall decrease in impedance and an increase in the charge capacity up to 475% of the initial value within three weeks.

A new approach of inducing proprioceptive illusion by transcutaneous electrical stimulation

BACKGROUND

Neurotraumas or neurodegenerative diseases often result in proprioceptive deficits, which makes it challenging for the nervous system to adapt to the compromised sensorimotor conditions. Also, in human machine interactions, such as prosthesis control and teleoperation, proprioceptive mismatch limits accuracy and intuitiveness of controlling active joints in robotic agents. To address these proprioceptive deficits, several invasive and non-invasive approaches like vibration, electrical nerve stimulation, and skin stretch have been introduced. However, proprioceptive modulation is still challenging as the current solutions have limitations in terms of effectiveness, usability, and consistency. In this paper, we propose a new way of modulating proprioception using transcutaneous electrical stimulation. We hypothesized that transcutaneous electrical stimulation on elbow flexor muscles will induce illusion of elbow joint extension.

METHOD

Eight healthy human subjects participated in the study to test the hypothesis. Transcutaneous electrodes were placed on different locations targeting elbow flexor muscles on human subjects and experiments were conducted to identify the best locations for electrode placement, and best electrical stimulation parameters, to maximize induced proprioceptive effect. Arm matching experiments and Pinocchio illusion test were performed for quantitative and qualitative analysis of the observed effects. One-way repeated ANOVA test was performed on the data collected in arm matching experiment for statistical analysis.

RESULTS

We identified the best location for transcutaneous electrodes to induce the proprioceptive illusion, as one electrode on the muscle belly of biceps brachii short head and the other on the distal myotendinous junction of brachioradialis. The results for arm-matching and Pinocchio illusion tests showed that transcutaneous electrical stimulation using identified electrode location and electrical stimulation parameters evoked the illusion of elbow joint extension for all eight subjects, which supports our hypothesis. On average, subjects reported 6.81° angular illusion of elbow joint extension in arm-matching tests and nose elongated to 1.78 × height in Pinocchio illusion test.

CONCLUSIONS

Transcutaneous electrical stimulation, applied between the the synergistic elbow flexor muscles, consistently modulated elbow joint proprioception with the illusion of elbow joint extension, which has immense potential to be translated into various real-world applications, including neuroprosthesis, rehabilitation, teleoperation, mixed reality, and etc.

Computational approaches to decode grasping force and velocity level in upper-limb amputee from intraneural peripheral signals

Objective. Recent results have shown the potentials of neural interfaces to provide sensory feedback to subjects with limb amputation increasing prosthesis usability. However, their advantages for decoding motor control signals over current methods based on electromyography (EMG) are still debated. In this study we compared a standard EMG-based method with approaches that use peripheral intraneural data to infer distinct levels of grasping force and velocity in a trans-radial amputee.Approach. Surface EMG (three channels) and intraneural signals (collected with transverse intrafascicular multichannel electrodes, TIMEs, 56 channels) were simultaneously recorded during the amputee's intended grasping movements. We sorted single unit activity (SUA) from each neural signal and then we identified the most informative units. EMG envelopes were extracted from the recorded EMG signals. A reference support vector machine (SVM) classifier was used to map EMG envelopes into desired force and velocity levels. Two decoding approaches using SUA were then tested and compared to the EMG-based reference classifier: (a) SVM classification of firing rates into desired force and velocity levels; (b) reconstruction of covariates (the grasp cue level or EMG envelopes) from neural data and use of covariates for classification into desired force and velocity levels.Main results.Using EMG envelopes as reconstructed covariates from SUA yielded significantly better results than the other approaches tested, with performance similar to that of the EMG-based reference classifier, and stable over three different recording days. Of the two reconstruction algorithms used in this approach, a linear Kalman filter and a nonlinear point process adaptive filter, the nonlinear filter gave better results.Significance.This study presented a new effective approach for decoding grasping force and velocity from peripheral intraneural signals in a trans-radial amputee, which relies on using SUA to reconstruct EMG envelopes. Being dependent on EMG recordings only for the training phase, this approach can fully exploit the advantages of implanted neural interfaces and potentially overcome, in the medium to long term, current state-of-the-art methods. (Clinical trial's registration number: NCT02848846).

Improving the Selectivity of an Osseointegrated Neural Interface: Proof of Concept For Housing Sieve Electrode Arrays in the Medullary Canal of Long Bones

Sieve electrodes stand poised to deliver the selectivity required for driving advanced prosthetics but are considered inherently invasive and lack the stability required for a chronic solution. This proof of concept experiment investigates the potential for the housing and engagement of a sieve electrode within the medullary canal as part of an osseointegrated neural interface (ONI) for greater selectivity toward improving prosthetic control. The working hypotheses are that (A) the addition of a sieve interface to a cuff electrode housed within the medullary canal of the femur as part of an ONI would be capable of measuring efferent and afferent compound nerve action potentials (CNAPs) through a greater number of channels; (B) that signaling improves over time; and (C) that stimulation at this interface generates measurable cortical somatosensory evoked potentials through a greater number of channels. The modified ONI was tested in a rabbit (n = 1) amputation model over 12 weeks, comparing the sieve component to the cuff, and subsequently compared to historical data. Efferent CNAPs were successfully recorded from the sieve demonstrating physiological improvements in CNAPs between weeks 3 and 5, and somatosensory cortical responses recorded at 12 weeks postoperatively. This demonstrates that sieve electrodes can be housed and function within the medullary canal, demonstrated by improved nerve engagement and distinct cortical sensory feedback. This data presents the conceptual framework for housing more sophisticated sieve electrodes in bone as part of an ONI for improving selectivity with percutaneous connectivity toward improved prosthetic control.

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.

Experimental Basis for Creating an Osseointegrated Neural Interface for Prosthetic Control: A Pilot Study in Rabbits

INTRODUCTION

While debate persists over how to best prevent or treat amputation neuromas, the more pressing question of how to best marry residual nerves to state-of-the-art robotic prostheses for naturalistic control of a replacement limb has come to the fore. One potential solution involves the transposition of terminal nerve ends into the medullary canal of long bones, creating the neural interface within the bone. Nerve transposition into bone is a long-practiced, clinically relevant treatment for painful neuromas. Despite neuropathic pain relief, the physiological capacity of transposed nerves to conduct motor and sensory signals required for prosthesis control remains unknown. This pilot study addresses the hypotheses that (1) bone provides stability to transposed nerves and (2) nerves transposed into bone remain physiologically active, as they relate to the creation of an osseointegrated neural interface.

METHODS

New Zealand white rabbits received transfemoral amputation, with the sciatic nerve transposed into the femur.

RESULTS

Morphological examination demonstrates that nerves remain stable within the medullary canal, while compound nerve action potentials evoked by electrical stimulation of the residual nerve within the bone could be achieved at 12 weeks (p < 0.0005).

CONCLUSION

Transposed nerves retain a degree of physiological function suitable for creating an osseointegrated neural interface.

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 identifier: NCT02994160).

Effect of stimulation duration to the existence of initial dip

In this paper, we investigate the effect of stimulation durations on the hemodynamic responses (HRs) in the somatosensory cortex. In doing so, the relationship between stimulation duration and the initial dip is also investigated. The HRs are measured using functional near-infrared spectroscopy (fNIRS). The HR signals related to finger poking are acquired from the left somatosensory cortex. Two different stimulation durations (i.e., 1 and 5 sec) were tested in this study. From the results of the study, it is concluded that the stimulation duration of 1 sec (short stimulus) evokes initial dip in the somatosensory cortex, but it disappears as the stimulation duration gets longer. Therefore, the 1-sec stimulation duration can serve the purpose of the fNIRS-based brain-computer interface.

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at 13 specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20-60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.

Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review

The field of prosthetics has been evolving and advancing over the past decade, as patients with missing extremities are expecting to control their prostheses in as normal a way as possible. Scientists have attempted to satisfy this expectation by designing a connection between the nervous system of the patient and the prosthetic limb, creating the field of neuroprosthetics. In this paper, we broadly review the techniques used to bridge the patient's peripheral nervous system to a prosthetic limb. First, we describe the electrical methods including myoelectric systems, surgical innovations and the role of nerve electrodes. We then describe non-electrical methods used alone or in combination with electrical methods. Design concerns from an engineering point of view are explored, and novel improvements to obtain a more stable interface are described. Finally, a critique of the methods with respect to their long-term impacts is provided. In this review, nerve electrodes are found to be one of the most promising interfaces in the future for intuitive user control. Clinical trials with larger patient populations, and for longer periods of time for certain interfaces, will help to evaluate the clinical application of nerve electrodes.

Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

An intrinsic problem when using hemodynamic responses for the brain-machine interface is the slow nature of the physiological process. In this paper, a novel method that estimates the oxyhemoglobin changes caused by neuronal activations is proposed and validated. In monitoring the time responses of blood-oxygen-level-dependent signals with functional near-infrared spectroscopy (fNIRS), the early trajectories of both oxy- and deoxy-hemoglobins in their phase space are scrutinized. Furthermore, to reduce the detection time, a prediction method based upon a kernel-based recursive least squares (KRLS) algorithm is implemented. In validating the proposed approach, the fNIRS signals of finger tapping tasks measured from the left motor cortex are examined. The results show that the KRLS algorithm using the Gaussian kernel yields the best fitting for both ΔHbO (i.e., 87.5%) and ΔHbR (i.e., 85.2%) at q = 15 steps ahead (i.e., 1.63 s ahead at a sampling frequency of 9.19 Hz). This concludes that a neuronal activation can be concluded in about 0.1 s with fNIRS using prediction, which enables an almost real-time practice if combined with EEG.

A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition

Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control.

A rat model for assessing the long-term safety and performance of peripheral nerve electrode arrays

BACKGROUND

High-resolution peripheral nerve interfaces (PNIs) can provide amputees with intuitive motor control and sensory feedback. Current PNIs are limited by early device failure and suboptimal long-term stability. The present study aims to incorporate functional assessment into an in vivo test platform to assess the long-term safety and performance of PNIs for recording and stimulation.

NEW METHODS

Utah electrode arrays (EA) were implanted in the rat sciatic nerve along with electromyography wires in the gastrocnemius and tibialis anterior. Cranial EEG screws were implanted in the somatosensory cortex for 12 weeks. Spontaneous neural activity was recorded using the implanted EA and stimulation-induced activity was monitored throughout the experiment. The impedance of each electrode was measured, and nerve function tests were conducted throughout the EA lifetime. Post-hoc safety assessments included scanning electron microscopy (SEM) of the EA and nerve histomorphometric analysis.

RESULTS

EA recordings were stable, and stimulation with EA elicited somatosensory evoked potentials and muscle contractions. Motor and sensory function tests indicated minimal deficits. Histomorphometric analysis indicated changes in nerve microstructure. SEM indicated EA-tip fracture, while lead wire breakage primarily caused device failure.

COMPARISON WITH EXISTING METHODS

We improved our prior platform with the addition of functional assessments of sensory pathways, a robust EMG array design to increase device longevity, and quantitative analysis of nerve microstructure.

CONCLUSION

We present a test platform for long-term assessment of peripheral nerve interfaces for stimulation and recording. Using this platform, we demonstrate recording and stimulation with minimal impact on nerve function, while EA lead wire breakage and tip fracture could limit long-term device use.

Evaluation of Neural Degeneration Biomarkers in the Prefrontal Cortex for Early Identification of Patients With Mild Cognitive Impairment: An fNIRS Study

Mild cognitive impairment (MCI), a condition characterizing poor cognition, is associated with aging and depicts early symptoms of severe cognitive impairment, known as Alzheimer's disease (AD). Meanwhile, early detection of MCI can prevent progression to AD. A great deal of research has been performed in the past decade on MCI detection. However, availability of biomarkers for MCI detection requires greater attention. In our study, we evaluated putative and reliable biomarkers for diagnosing MCI by performing different mental tasks (i.e., N-back task, Stroop task, and verbal fluency task) using functional near-infrared spectroscopy (fNIRS) signals on a group of 15 MCI patients and 9 healthy control (HC). The 15 digital biomarkers (i.e., five means, seven slopes, peak, skewness, and kurtosis) and two image biomarkers (t-map, correlation map) in the prefrontal cortex (PFC) (i.e., left PFC, middle PFC, and right PFC) between the MCI and HC groups were investigated by the statistical analysis, linear discriminant analysis (LDA), and convolutional neural network (CNN) individually. The results reveal that the statistical analysis using digital biomarkers (with a p-value < 0.05) could not distinguish the MCI patients from the HC over 60% accuracy. Therefore, the current statistical analysis needs to be improved to be used for diagnosing the MCI patients. The best accuracy with LDA was 76.67% with the N-back and Stroop tasks. However, the CNN classification results trained by image biomarkers showed a high accuracy. In particular, the CNN results trained via t-maps revealed the best accuracy (90.62%) with the N-back task, whereas the CNN result trained by the correlation maps was 85.58% with the N-back task. Also, the results illustrated that investigating the sub-regions (i.e., right, middle, left) of the PFC for detecting MCI would be better than examining the whole PFC. The t-map (or/and the correlation map) is conclusively recommended as an image biomarker for early detection of AD. The combination of CNN and image biomarkers can provide a reliable clinical tool for diagnosing MCI patients.

Effects of Acupuncture Therapy on MCI Patients Using Functional Near-Infrared Spectroscopy

Acupuncture therapy (AT) is a non-pharmacological method of treatment that has been applied to various neurological diseases. However, studies on its longitudinal effect on the neural mechanisms of patients with mild cognitive impairment (MCI) for treatment purposes are still lacking in the literature. In this clinical study, we assess the longitudinal effects of ATs on MCI patients using two methods: (i) Montreal Cognitive Assessment test (MoCA-K, Korean version), and (ii) the hemodynamic response (HR) analyses using functional near-infrared spectroscopy (fNIRS). fNIRS signals of a working memory (WM) task were acquired from the prefrontal cortex. Twelve elderly MCI patients and 12 healthy people were recruited as target and healthy control (HC) groups, respectively. Each group went through an fNIRS scanning procedure three times: The initial data were obtained without any ATs, and subsequently a total of 24 AT sessions were conducted for MCI patients (i.e., MCI-0: the data prior to ATs, MCI-1: after 12 sessions of ATs for 6 weeks, MCI-2: another 12 sessions of ATs for 6 weeks). The mean HR responses of all MCI-0–2 cases were lower than those of HCs. To compare the effects of AT on MCI patients, MoCA-K results, temporal HR data, and spatial activation patterns (i.e., t-maps) were examined. In addition, analyses of functional connectivity (FC) and graph theory upon WM tasks were conducted. With ATs, (i) the averaged MoCA-K test scores were improved (MCI-1, p = 0.002; MCI-2, p = 2.9e^–4); (ii) the mean HR response of WM tasks was increased (p < 0.001); and (iii) the t-maps of MCI-1 and MCI-2 were enhanced. Furthermore, an increased FC in the prefrontal cortex in both MCI-1/MCI-2 cases in comparison to MCI-0 was obtained (p < 0.01), and an increasing trend in the graph theory parameters was observed. All these findings reveal that ATs have a positive impact on improving the cognitive function of MCI patients. In conclusion, ATs can be used as a therapeutic tool for MCI patients as a non-pharmacological method (Clinical trial registration number: KCT 0002451 https://cris.nih.go.kr/cris/en/).

Functional Spectroscopy Mapping of Pain Processing Cortical Areas During Non-painful Peripheral Electrical Stimulation of the Accessory Spinal Nerve

Peripheral electrical stimulation (PES), which encompasses several techniques with heterogeneous physiological responses, has shown in some cases remarkable outcomes for pain treatment and clinical rehabilitation. However, results are still mixed, mainly because there is a lack of understanding regarding its neural mechanisms of action. In this study, we aimed to assess its effects by measuring cortical activation as indexed by functional near infrared spectroscopy (fNIRS). fNIRS is a functional optical imaging method to evaluate hemodynamic changes in oxygenated (HbO) and de-oxygenated (HbR) blood hemoglobin concentrations in cortical capillary networks that can be related to cortical activity. We hypothesized that non-painful PES of accessory spinal nerve (ASN) can promote cortical activation of sensorimotor cortex (SMC) and dorsolateral prefrontal cortex (DLPFC) pain processing cortical areas. Fifteen healthy volunteers received both active and sham ASN electrical stimulation in a crossover study. The hemodynamic cortical response to unilateral right ASN burst electrical stimulation with 10 Hz was measured by a 40-channel fNIRS system. The effect of ASN electrical stimulation over HbO concentration in cortical areas of interest (CAI) was observed through the activation of right-DLPFC (p = 0.025) and left-SMC (p = 0.042) in the active group but not in sham group. Regarding left-DLPFC (p = 0.610) and right-SMC (p = 0.174) there was no statistical difference between groups. As in non-invasive brain stimulation (NIBS) top-down modulation, bottom-up electrical stimulation to the ASN seems to activate the same critical cortical areas on pain pathways related to sensory-discriminative and affective-motivational pain dimensions. These results provide additional mechanistic evidence to develop and optimize the use of peripheral nerve electrical stimulation as a neuromodulatory tool (NCT 03295370— www.clinicaltrials.gov).

An Intrafascicular Neural Interface With Enhanced Interconnection for Recording of Peripheral Nerve Signals

For implantable devices, Parylene C (hereafter referred to as Parylene) has shown promising properties such as flexibility, biocompatibility, biostability, and good barrier properties. Parylene-based flexible interconnection cable (FIC) was previously developed to connect a flexible penetrating microelectrode array (FPMA) with a recording system. However, Parylene-based FIC was difficult to handle and prone to damage during the implantation surgery because of its low mechanical strength. To improve the mechanical properties of the FIC, we suggest a mechanically enhanced flexible interconnection cable (enhanced FIC) obtained using a combination of Parylene and polyimide. To investigate the long-term stability of the enhanced FIC, Parylene-only FIC, and enhanced FIC were tested and their mechanical properties were compared under an accelerated aging condition. During the course of six months of soaking, the maximum strength of the enhanced FIC remained twice as high as that of the Parylene-only FIC throughout the experiment, although the mechanical strength of both FICs decreased over time. To show the capability of the enhanced FIC in the context of nerve signal recording as a part of a neural interfacing device, it was assembled together with the FPMA and custom-made wireless recording electronics. We demonstrated the feasibility of the enhanced FIC in an in vivo application by recording acute nerve signals from canine sciatic nerves.

Existence of Initial Dip for BCI: An Illusion or Reality

A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.

Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh-Nagumo Neurons under Direction-Dependent Coupling

This paper exploits the dynamical modeling, behavior analysis, and synchronization of a network of four different FitzHugh–Nagumo (FHN) neurons with unknown parameters linked in a ring configuration under direction-dependent coupling. The main purpose is to investigate a robust adaptive control law for the synchronization of uncertain and perturbed neurons, communicating in a medium of bidirectional coupling. The neurons are assumed to be different and interconnected in a ring structure. The strength of the gap junctions is taken to be different for each link in the network, owing to the inter-neuronal coupling medium properties. Robust adaptive control mechanism based on Lyapunov stability analysis is employed and theoretical criteria are derived to realize the synchronization of the network of four FHN neurons in a ring form with unknown parameters under direction-dependent coupling and disturbances. The proposed scheme for synchronization of dissimilar neurons, under external electrical stimuli, coupled in a ring communication topology, having all parameters unknown, and subject to directional coupling medium and perturbations, is addressed for the first time as per our knowledge. To demonstrate the efficacy of the proposed strategy, simulation results are provided.

Motor-commands decoding using peripheral nerve signals: a review

During the last few decades, substantial scientific and technological efforts have been focused on the development of neuroprostheses. The major emphasis has been on techniques for connecting the human nervous system with a robotic prosthesis via natural-feeling interfaces. The peripheral nerves provide access to highly processed and segregated neural command signals from the brain that can in principle be used to determine user intent and control muscles. If these signals could be used, they might allow near-natural and intuitive control of prosthetic limbs with multiple degrees of freedom. This review summarizes the history of neuroprosthetic interfaces and their ability to record from and stimulate peripheral nerves. We also discuss the types of interfaces available and their applications, the kinds of peripheral nerve signals that are used, and the algorithms used to decode them. Finally, we explore the prospects for future development in this area.