Staying in Touch: Toward the Restoration of Sensory Feedback in Hand Prostheses Using Peripheral Neural Stimulation

Fork-shaped neural interface with multichannel high spatial selectivity in the peripheral nerve of a rat

Objective.This study aims to develop and validate a sophisticated fork-shaped neural interface (FNI) designed for peripheral nerves, focusing on achieving high spatial resolution, functional selectivity, and improved charge storage capacities. The objective is to create a neurointerface capable of precise neuroanatomical analysis, neural signal recording, and stimulation.Approach.Our approach involves the design and implementation of the FNI, which integrates 32 multichannel working electrodes featuring enhanced charge storage capacities and low impedance. An insertion guide holder is incorporated to refine neuronal selectivity. The study employs meticulous electrode placement, bipolar electrical stimulation, and comprehensive analysis of induced neural responses to verify the FNI's capabilities. Stability over an eight-week period is a crucial aspect, ensuring the reliability and durability of the neural interface.Main results.The FNI demonstrated remarkable efficacy in neuroanatomical analysis, exhibiting accurate positioning of motor nerves and successfully inducing various movements. Stable impedance values were maintained over the eight-week period, affirming the durability of the FNI. Additionally, the neural interface proved effective in recording sensory signals from different hind limb areas. The advanced charge storage capacities and low impedance contribute to the FNI's robust performance, establishing its potential for prolonged use.Significance.This research represents a significant advancement in neural interface technology, offering a versatile tool with broad applications in neuroscience and neuroengineering. The FNI's ability to capture both motor and sensory neural activity positions it as a comprehensive solution for neuroanatomical studies. Moreover, the precise neuromodulation potential of the FNI holds promise for applications in advanced bionic prosthetic control and therapeutic interventions. The study's findings contribute to the evolving field of neuroengineering, paving the way for enhanced understanding and manipulation of peripheral neural functions.

Biomimetic Approaches for Separated Regeneration of Sensory and Motor Fibers in Amputee People: Necessary Conditions for Functional Integration of Sensory-Motor Prostheses With the Peripheral Nerves

The regenerative capacity of the peripheral nervous system after an injury is limited, and a complete function is not recovered, mainly due to the loss of nerve tissue after the injury that causes a separation between the nerve ends and to the disorganized and intermingled growth of sensory and motor nerve fibers that cause erroneous reinnervations. Even though the development of biomaterials is a very promising field, today no significant results have been achieved. In this work, we study not only the characteristics that should have the support that will allow the growth of nerve fibers, but also the molecular profile necessary for a specific guidance. To do this, we carried out an exhaustive study of the molecular profile present during the regeneration of the sensory and motor fibers separately, as well as of the effect obtained by the administration and inhibition of different factors involved in the regeneration. In addition, we offer a complete design of the ideal characteristics of a biomaterial, which allows the growth of the sensory and motor neurons in a differentiated way, indicating (1) size and characteristics of the material; (2) necessity to act at the microlevel, on small groups of neurons; (3) combination of molecules and specific substrates; and (4) temporal profile of those molecules expression throughout the regeneration process. The importance of the design we offer is that it respects the complexity and characteristics of the regeneration process; it indicates the appropriate temporal conditions of molecular expression, in order to obtain a synergistic effect; it takes into account the importance of considering the process at the group of neuron level; and it gives an answer to the main limitations in the current studies.

Cortical connectivity and spectral perturbations underlying TENS stimulation of hand nerves: a case study

The major challenge in upper limbs neuroprosthetic improvement is the implementation of effective sensory feedback. Transcutaneous electrical nerve stimulation (TENS) of the median and ulnar nerves confirmed, with electroencephalographic (EEG) recordings, the presence of appropriate responses in relevant cortical areas with induced sensation successfully located in the innervation regions of each nerve. The characterization of these elicited responses could be used to recreate precise somatotopic feedback from hand protheses. Using TENS and EEG, the purpose of this study was to detect distinctions in time-frequency cortical dynamics and connectivity occurring after stimulation of hand nerves. Region of interest (ROI) were selected according to topographical distributions and Somatosensory Evoked Potentials (SEP) localization and were named Contralateral Parietal (Cont P), Central Frontal (Cent F) and Superior Parietal (Sup P). The analysis of cortical oscillations showed spectral inflections in theta [4-7 Hz] and alpha [7.5-12.5 Hz] band which occurred at 60 ms in Cont P and 300 ms in Sup P and prominent for the ulnar condition over the median one. The beta band decrease [16-30 Hz] which occurred in the same ROIs was especially significant after ulnar stimulation too. Effective connectivity measures did not differ significantly across conditions but exhibited some slight difference in the alpha-band causal flow coming from Cent F in direction to Cont P and Sup P. Although pending completion of multiple-subjects study, these results already suggest magnitude differences in somatosensory spectral fluctuations and sensorimotor interactions flows.

Spatiotemporal characteristics of neural activity in tibial nerves with carbon nanotube yarn electrodes

BACKGROUND

Reliable interfacing with peripheral nervous system is essential to extract neural signals. Current implantable peripheral nerve electrodes cannot provide long-term reliable interfaces due to their mechanical mismatch with host nerves. Carbon nanotube (CNT) yarns possess excellent mechanical flexibility and electrical conductivity. It is of great necessity to investigate the selectivity of implantable CNT yarn electrodes.

NEW METHOD

Neural interfaces were fabricated with CNT yarn electrodes insulated with Parylene-C. Acute recordings were carried out on tibial nerves of rats, and compound nerve action potentials (CNAPs) were electrically evoked by biphasic current stimulation of four toes. Spatiotemporal characteristics of neural activity and spatial selectivity of the electrodes, denoted by selectivity index (SI), were analyzed in detail.

RESULTS

Conduction velocities of sensory afferent fibers recorded by CNT yarn electrodes varied between 4.25 m/s and 37.56 m/s. The SI maxima for specific toes were between 0.55 and 0.99 across seven electrodes. SIs for different CNT yarn electrodes are significantly different among varied toes.

COMPARISON WITH EXISTING METHODS

Most single CNT yarn electrode with a ∼ 500 μm exposed length can be sensitive to one or two specific toes in rodent animals. While, it is only possible to discriminate two non-adjacent toes by multisite TIME electrodes.

CONCLUSION

Single CNT yarn electrode exposed ∼ 500 μm showed SI values for different toes comparable to a multisite TIME electrode, and had high spatial selectivity for one or two specific toes. The electrodes with cross section exposed could intend to be more sensitive to one specific toe.

Realizing Efficient EMG-Based Prosthetic Control Strategy

As an integral part of the body, the limb poses dexterous and fine motor grasping and sensing capabilities that enable humans to effectively communicate with their environment during activities of daily living (ADL). Hence, limb loss severely limits individuals' ability especially when they need to perform tasks requiring their limb functions during ADL, thus leading to decreased quality of life. To effectively restore limb functions in amputees, the advanced prostheses that are controlled by electromyography (EMG) signal have been widely investigated and used. Since EMG signals reflect neural activity, they would contain information on the muscle activation related to limb motions. Pattern recognition-based myoelectric control is an important branch of the EMG-based prosthetic control. And the EMG-based prosthetic control theoretically supports multiple degrees of freedom movements  that allows amputees to intuitively manipulate the device. This chapter focuses on EMG-based prosthetic control strategy that involves utilizing intelligent computational technique to decode upper limb movement intentions from which control commands are derived. Additionally, different techniques/methods for improving the overall performance of EMG-based prostheses control strategy were introduced and discussed in this chapter.

PCA and deep learning based myoelectric grasping control of a prosthetic hand

Background

For the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic hand will be greatly improved if the stable grip of prosthetic hand can be achieved. To address this problem, in this study, a bio-signal control method for grasping control of a prosthetic hand is proposed to improve patient’s sense of using prosthetic hand and the thus improving the quality of life.

Methods

A MYO gesture control armband is used to collect the surface electromyographic (sEMG) signals from the upper limb. The overlapping sliding window scheme are applied for data segmentation and the correlated features are extracted from each segmented data. Principal component analysis (PCA) methods are then deployed for dimension reduction. Deep neural network is used to generate sEMG-force regression model for force prediction at different levels. The predicted force values are input to a fuzzy controller for the grasping control of a prosthetic hand. A vibration feedback device is used to feed grasping force value back to patient’s arm to improve patient’s sense of using prosthetic hand and realize accurate grasping. To test the effectiveness of the scheme, 15 able-bodied subjects participated in the experiments.

Results

The classification results indicated that 8-channel sEMG applying all four time-domain features, with PCA reduction from 32 to 8 dimensions results in the highest classification accuracy. Based on the experimental results from 15 participants, the average recognition rate is over 95%. On the other hand, from the statistical results of standard deviation, the between-subject variations ranges from 3.58 to 1.25%, proving that the robustness and stability of the proposed approach.

Conclusions

The method proposed hereto control grasping power through the patient’s own sEMG signal, which achieves a high recognition rate to improve the success rate of grip and increases the sense of operation and also brings the gospel for upper extremity amputation patients.

Rapid prototyping of flexible intrafascicular electrode arrays by picosecond laser structuring

OBJECTIVE

Interfacing the peripheral nervous system can be performed with a large variety of electrode arrays. However, stimulating and recording a nerve while having a reasonable amount of channels limits the number of available systems. Translational research towards human clinical trial requires device safety and biocompatibility but would benefit from design flexibility in the development process to individualize probes.

APPROACH

We selected established medical grade implant materials like precious metals and Parylene C to develop a rapid prototyping process for novel intrafascicular electrode arrays using a picosecond laser structuring. A design for a rodent animal model was developed in conjunction with an intrafascicular implantation strategy. Electrode characterization and optimization was performed first in saline solution in vitro before performance and biocompatibility were validated in sciatic nerves of rats in chronic implantation.

MAIN RESULTS

The novel fabrication process proved to be suitable for prototyping and building intrafascicular electrode arrays. Electrochemical properties of the electrode sites were enhanced and tested for long-term stability. Chronic implantation in the sciatic nerve of rats showed good biocompatibility, selectivity and stable stimulation thresholds.

SIGNIFICANCE

Established medical grade materials can be used for intrafascicular nerve electrode arrays when laser structuring defines structure size in the micro-scale. Design flexibility reduces re-design cycle time and material certificates are beneficial support for safety studies on the way to clinical trials.

Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.