The Use of the Velocity Selective Recording Technique to Reveal the Excitation Properties of the Ulnar Nerve in Pigs

Decoding information from the peripheral nervous system via implantable neural interfaces remains a significant challenge, considerably limiting the advancement of neuromodulation and neuroprosthetic devices. The velocity selective recording (VSR) technique has been proposed to improve the classification of neural traffic by combining temporal and spatial information through a multi-electrode cuff (MEC). Therefore, this study investigates the feasibility of using the VSR technique to characterise fibre type based on the electrically evoked compound action potentials (eCAP) propagating along the ulnar nerve of pigs in vivo. A range of electrical stimulation parameters (amplitudes of 50 μA–10 mA and pulse durations of 100 μs, 500 μs, 1000 μs, and 5000 μs) was applied on a cutaneous and a motor branch of the ulnar nerve in nine Danish landrace pigs. Recordings were made with a 14 ring MEC and a delay-and-add algorithm was used to convert the eCAPs into the velocity domain. The results revealed two fibre populations propagating along the cutaneous branch of the ulnar nerve, with mean velocities of 55 m/s and 21 m/s, while only one dominant fibre population was found for the motor branch, with a mean velocity of 63 m/s. Because of its simplicity to provide information on the fibre selectivity and direction of propagation of nerve fibres, VSR can be implemented to advance the performance of the bidirectional control of neural prostheses and bioelectronic medicine applications.

Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition

The recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. This development leads to assist people with disabilities to benefit from neuroprosthetic devices that improve the life of those suffering from neurological disorders. This paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. Therefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. The paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).

Locus coeruleus activity improves cochlear implant performance

Cochlear implants are neuroprosthetic devices that can provide hearing to deaf patients1. Despite significant benefits offered by cochlear implants, there are highly variable outcomes in how quickly hearing is restored and perceptual accuracy after months or years of use2,3. Cochlear implant use is believed to require neuroplasticity within the central auditory system, and differential engagement of neuroplastic mechanisms might contribute to outcome variability4-7. Despite extensive studies on how cochlear implants activate the auditory system4,8-12, our understanding of cochlear implant-related neuroplasticity remains limited. One potent factor enabling plasticity is the neuromodulator norepinephrine from the brainstem locus coeruleus. Here we examined behavioral responses and neural activity in locus coeruleus and auditory cortex of deafened rats fitted with multi-channel cochlear implants. Animals were trained on a reward-based auditory task, with considerable individual differences of learning rates and maximum performance. Photometry from locus coeruleus predicted when implanted subjects would begin responding to sounds and longer-term perceptual accuracy, which were augmented by optogenetic locus coeruleus stimulation. Auditory cortical responses to cochlear implant stimulation reflected behavioral performance, with enhanced responses to rewarded stimuli and decreased distinction between unrewarded stimuli. Adequate engagement of central neuromodulatory systems is thus a potential clinically-relevant target for optimizing neuroprosthetic device use.

Locus coeruleus activity improves cochlear implant performance

Cochlear implants are neuroprosthetic devices that can provide hearing to deaf patients1. Despite significant benefits offered by cochlear implants, there are highly variable outcomes in how quickly hearing is restored and perceptual accuracy after months or years of use2,3. Cochlear implant use is believed to require neuroplasticity within the central auditory system, and differential engagement of neuroplastic mechanisms might contribute to outcome variability4-7. Despite extensive studies on how cochlear implants activate the auditory system4,8-12, our understanding of cochlear implant-related neuroplasticity remains limited. One potent factor enabling plasticity is the neuromodulator norepinephrine from the brainstem locus coeruleus. Here we examined behavioral responses and neural activity in locus coeruleus and auditory cortex of deafened rats fitted with multi-channel cochlear implants. Animals were trained on a reward-based auditory task, with considerable individual differences of learning rates and maximum performance. Photometry from locus coeruleus predicted when implanted subjects would begin responding to sounds and longer-term perceptual accuracy, which were augmented by optogenetic locus coeruleus stimulation. Auditory cortical responses to cochlear implant stimulation reflected behavioral performance, with enhanced responses to rewarded stimuli and decreased distinction between unrewarded stimuli. Adequate engagement of central neuromodulatory systems is thus a potential clinically-relevant target for optimizing neuroprosthetic device use.

Restoration of bilateral motor coordination from preserved agonist-antagonist coupling in amputation musculature

Background Neuroprosthetic devices controlled by persons with standard limb amputation often lack the dexterity of the physiological limb due to limitations of both the user’s ability to output accurate control signals and the control system’s ability to formulate dynamic trajectories from those signals. To restore full limb functionality to persons with amputation, it is necessary to first deduce and quantify the motor performance of the missing limbs, then meet these performance requirements through direct, volitional control of neuroprosthetic devices. Methods We develop a neuromuscular modeling and optimization paradigm for the agonist-antagonist myoneural interface, a novel tissue architecture and neural interface for the control of myoelectric prostheses, that enables it to generate virtual joint trajectories coordinated with an intact biological joint at full physiologically-relevant movement bandwidth. In this investigation, a baseline of performance is first established in a population of non-amputee control subjects ($$n = 8$$ n = 8 ). Then, a neuromuscular modeling and optimization technique is advanced that allows unilateral AMI amputation subjects ($$n = 5$$ n = 5 ) and standard amputation subjects ($$n = 4$$ n = 4 ) to generate virtual subtalar prosthetic joint kinematics using measured surface electromyography (sEMG) signals generated by musculature within the affected leg residuum. Results Using their optimized neuromuscular subtalar models under blindfolded conditions with only proprioceptive feedback, AMI amputation subjects demonstrate bilateral subtalar coordination accuracy not significantly different from that of the non-amputee control group (Kolmogorov-Smirnov test, $$P \ge 0.052$$ P ≥ 0.052 ) while standard amputation subjects demonstrate significantly poorer performance (Kolmogorov-Smirnov test, $$P < 0.001$$ P < 0.001 ). Conclusions These results suggest that the absence of an intact biological joint does not necessarily remove the ability to produce neurophysical signals with sufficient information to reconstruct physiological movements. Further, the seamless manner in which virtual and intact biological joints are shown to coordinate reinforces the theory that desired movement trajectories are mentally formulated in an abstract task space which does not depend on physical limb configurations.

An Ultrasound Imaging Based Observer for Estimating NMES-Induced Muscle Fatigue: Theory and Simulation

Neuroprosthetic devices that use transcutaneous neuromuscular electrical stimulation (NMES) are potential interventions to restore skeletal muscle function in people with neurological disorders. As commonly noted, how to assess the NMES-induced muscle fatigue is a critical problem. This is because the capability of fatigue assessment is a necessary precursor for optimally modulating the NMES dosage to improve the control performance of a neuroprosthesis and ensure user’s safety. To effectively estimate the NMES-induced muscle fatigue, this paper proposes a novel state observer that combines a mathematical predictive fatigue model and intermittent feedback from ultrasound-derived strain images. The strain images quantify muscle contractility during NMES. Principal component regression (PCR) is used to derive a relationship between the strain images and instantaneous muscle force production. Lyapunov stability analysis was performed to obtain the convergence property of the designed observer. A globally uniformly ultimately bounded (GUUB) result was obtained. Simulations based on pre-recorded data from a human experiment were also conducted to demonstrate the performance of the designed observer.

Imaging of Auditory Brain Stem Implants

Auditory brain stem implants are infrequently encountered neuroprosthetic devices used for auditory rehabilitation in deaf patients with pathology between the cochlea and cochlear nuclei who would not benefit from cochlear implantation. This article reviews the device, the relevant anatomy, audiologic performance, operative approaches, and conditions in which auditory brain stem implants are indicated. The imaging appearance of auditory brain stem implants, including optimal lead positioning, and imaging safety considerations of the device are also discussed. Knowledge of the device can assist the radiologist in detecting postoperative complications and component malpositioning and in providing safe and effective imaging practices in patients with indwelling auditory brain stem implants.Learning Objective: To describe the auditory brain stem implant device, identify optimal lead positioning, and list indications for auditory brain stem implant placement.

Injecting Information into the Mammalian Cortex: Progress, Challenges, and Promise

For 150 years artificial stimulation has been used to study the function of the nervous system. Such stimulation—whether electrical or optogenetic—eventually may be used in neuroprosthetic devices to replace lost sensory inputs and to otherwise introduce information into the nervous system. Efforts toward this goal can be classified broadly as either biomimetic or arbitrary. Biomimetic stimulation aims to mimic patterns of natural neural activity, so that the subject immediately experiences the artificial stimulation as if it were natural sensation. Arbitrary stimulation, in contrast, makes no attempt to mimic natural patterns of neural activity. Instead, different stimuli—at different locations and/or in different patterns—are assigned different meanings randomly. The subject’s time and effort then are required to learn to interpret different stimuli, a process that engages the brain’s inherent plasticity. Here we will examine progress in using artificial stimulation to inject information into the cerebral cortex and discuss the challenges for and the promise of future development.

Generating Artificial Sensations with Spinal Cord Stimulation in Primates and Rodents

For patients who have lost sensory function due to a neurological injury such as spinal cord injury (SCI), stroke, or amputation, spinal cord stimulation (SCS) may provide a mechanism for restoring somatic sensations via an intuitive, non-visual pathway. Inspired by this vision, here we trained rhesus monkeys and rats to detect and discriminate patterns of epidural SCS. Thereafter, we constructed psychometric curves describing the relationship between different SCS parameters and the animal’s ability to detect SCS and/or changes in its characteristics. We found that the stimulus detection threshold decreased with higher frequency, longer pulse-width, and increasing duration of SCS. Moreover, we found that monkeys were able to discriminate temporally- and spatially-varying patterns (i.e. variations in frequency and location) of SCS delivered through multiple electrodes. Additionally, sensory discrimination of SCS-induced sensations in rats obeyed Weber’s law of just noticeable differences. These findings suggest that by varying SCS intensity, temporal pattern, and location different sensory experiences can be evoked. As such, we posit that SCS can provide intuitive sensory feedback in neuroprosthetic devices.

The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on “An Integrated Brain-Machine Interface Platform With Thousands of Channels”

Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life (potentially due to issues with long-term biocompatibility), as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed.