Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human

Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review

STUDY DESIGN

Systematic review of the literature.

OBJECTIVES

In recent years, brain-computer interface (BCI) has emerged as a potential treatment for patients with spinal cord injury (SCI). This is the first systematic review of the literature on invasive closed-loop BCI technologies for the treatment of SCI in humans.

MATERIALS AND METHODS

A comprehensive search of PubMed MEDLINE, Web of Science, and Ovid EMBASE was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

RESULTS

Of 8316 articles collected, 19 studies met all the inclusion criteria. Data from 21 patients were extracted from these studies. All patients sustained a cervical SCI and were treated using either a BCI with intracortical microelectrode arrays (n = 18, 85.7%) or electrocorticography (n = 3, 14.3%). To decode these neural signals, machine learning and statistical models were used: support vector machine in eight patients (38.1%), linear estimator in seven patients (33.3%), Hidden Markov Model in three patients (14.3%), and other in three patients (14.3%). As the outputs, ten patients (47.6%) underwent noninvasive functional electrical stimulation (FES) with a cuff; one (4.8%) had an invasive FES with percutaneous stimulation, and ten (47.6%) used an external device (neuroprosthesis or virtual avatar). Motor function was restored in all patients for each assigned task. Clinical outcome measures were heterogeneous across all studies.

CONCLUSIONS

Invasive techniques of BCI show promise for the treatment of SCI, but there is currently no technology that can restore complete functional autonomy in patients with SCI. The current techniques and outcomes of BCI vary greatly. Because invasive BCIs are still in the early stages of development, further clinical studies should be conducted to optimize the prognosis for patients with SCI.

Intracortical brain-computer interfaces for improved motor function: a systematic review

In this systematic review, we address the status of intracortical brain-computer interfaces (iBCIs) applied to the motor cortex to improve function in patients with impaired motor ability. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guidelines for Systematic Reviews. Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) and the Effective Public Health Practice Project (EPHPP) were used to assess bias and quality. Advances in iBCIs in the last two decades demonstrated the use of iBCI to activate limbs for functional tasks, achieve neural typing for communication, and other applications. However, the inconsistency of performance metrics employed by these studies suggests the need for standardization. Each study was a pilot clinical trial consisting of 1-4, majority male (64.28 %) participants, with most trials featuring participants treated for more than 12 months (55.55 %). The systems treated patients with various conditions: amyotrophic lateral sclerosis, stroke, spinocerebellar degeneration without cerebellar involvement, and spinal cord injury. All participants presented with tetraplegia at implantation and were implanted with microelectrode arrays via pneumatic insertion, with nearly all electrode locations solely at the precentral gyrus of the motor cortex (88.88 %). The development of iBCI devices using neural signals from the motor cortex to improve motor-impaired patients has enhanced the ability of these systems to return ability to their users. However, many milestones remain before these devices can prove their feasibility for recovery. This review summarizes the achievements and shortfalls of these systems and their respective trials.

Rhesus monkeys learn to control a directional-key inspired brain machine interface via bio-feedback

Brain machine interfaces (BMI) connect brains directly to the outside world, bypassing natural neural systems and actuators. Neuronal-activity-to-motion transformation algorithms allow applications such as control of prosthetics or computer cursors. These algorithms lie within a spectrum between bio-mimetic control and bio-feedback control. The bio-mimetic approach relies on increasingly complex algorithms to decode neural activity by mimicking the natural neural system and actuator relationship while focusing on machine learning: the supervised fitting of decoder parameters. On the other hand, the bio-feedback approach uses simple algorithms and relies primarily on user learning, which may take some time, but can facilitate control of novel, non-biological appendages. An increasing amount of work has focused on the arguably more successful bio-mimetic approach. However, as chronic recordings have become more accessible and utilization of novel appendages such as computer cursors have become more universal, users can more easily spend time learning in a bio-feedback control paradigm. We believe a simple approach which leverages user learning and few assumptions will provide users with good control ability. To test the feasibility of this idea, we implemented a simple firing-rate-to-motion correspondence rule, assigned groups of neurons to virtual "directional keys" for control of a 2D cursor. Though not strictly required, to facilitate initial control, we selected neurons with similar preferred directions for each group. The groups of neurons were kept the same across multiple recording sessions to allow learning. Two Rhesus monkeys used this BMI to perform a center-out cursor movement task. After about a week of training, monkeys performed the task better and neuronal signal patterns changed on a group basis, indicating learning. While our experiments did not compare this bio-feedback BMI to bio-mimetic BMIs, the results demonstrate the feasibility of our control paradigm and paves the way for further research in multi-dimensional bio-feedback BMIs.

Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface

OBJECTIVE

Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs.

APPROACH

To address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations.

MAIN RESULTS

Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve's design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort.

SIGNIFICANCE

The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.

Effectiveness of the Combined Use of a Brain-Machine Interface System and Virtual Reality as a Therapeutic Approach in Patients with Spinal Cord Injury: A Systematic Review

Spinal cord injury has a major impact on both the individual and society. This damage can cause permanent loss of sensorimotor functions, leading to structural and functional changes in somatotopic regions of the spinal cord. The combined use of a brain-machine interface and virtual reality offers a therapeutic alternative to be considered in the treatment of this pathology. This systematic review aimed to evaluate the effectiveness of the combined use of virtual reality and the brain-machine interface in the treatment of spinal cord injuries. A search was performed in PubMed, Web of Science, PEDro, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, and Medline, including articles published from the beginning of each database until January 2023. Articles were selected based on strict inclusion and exclusion criteria. The Cochrane Collaboration's tool was used to assess the risk of bias and the PEDro scale and SCIRE systems were used to evaluate the methodological quality of the studies. Eleven articles were selected from a total of eighty-two. Statistically significant changes were found in the upper limb, involving improvements in shoulder and upper arm mobility, and weaker muscles were strengthened. In conclusion, most of the articles analyzed used the electroencephalogram as a measurement instrument for the assessment of various parameters, and most studies have shown improvements. Nonetheless, further research is needed with a larger sample size and long-term follow-up to establish conclusive results regarding the effect size of these interventions.

Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome

Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.

Implanted Electrodes for Functional Electrical Stimulation to Restore Upper and Lower Extremity Function: History and Future Directions

Functional electrical stimulation (FES) to activate nerves and muscles in paralyzed extremities has considerable promise to improve outcome after neurological disease or injury, especially in individuals who have upper motor nerve dysfunction due to central nervous system pathology. Because technology has improved, a wide variety of methods for providing electrical stimulation to create functional movements have been developed, including muscle stimulating electrodes, nerve stimulating electrodes, and hybrid constructs. However, in spite of decades of success in experimental settings with clear functional improvements for individuals with paralysis, the technology has not yet reached widespread clinical translation. In this review, we outline the history of FES techniques and approaches and describe future directions in evolution of the technology.

Functional electrical stimulation therapy controlled by a P300-based brain-computer interface, as a therapeutic alternative for upper limb motor function recovery in chronic post-stroke patients. A non-randomized pilot study

Introduction

Up to 80% of post-stroke patients present upper-limb motor impairment (ULMI), causing functional limitations in daily activities and loss of independence. UMLI is seldom fully recovered after stroke when using conventional therapeutic approaches. Functional Electrical Stimulation Therapy (FEST) controlled by Brain-Computer Interface (BCI) is an alternative that may induce neuroplastic changes, even in chronic post-stroke patients. The purpose of this work was to evaluate the effects of a P300-based BCI-controlled FEST intervention, for ULMI recovery of chronic post-stroke patients.

Methods

A non-randomized pilot study was conducted, including 14 patients divided into 2 groups: BCI-FEST, and Conventional Therapy. Assessments of Upper limb functionality with Action Research Arm Test (ARAT), performance impairment with Fugl-Meyer assessment (FMA), Functional Independence Measure (FIM) and spasticity through Modified Ashworth Scale (MAS) were performed at baseline and after carrying out 20 therapy sessions, and the obtained scores compared using Chi square and Mann-Whitney U statistical tests (𝛼 = 0.05).

Results

After training, we found statistically significant differences between groups for FMA (p = 0.012), ARAT (p < 0.001), and FIM (p = 0.025) scales.

Discussion

It has been shown that FEST controlled by a P300-based BCI, may be more effective than conventional therapy to improve ULMI after stroke, regardless of chronicity.

Conclusion

The results of the proposed BCI-FEST intervention are promising, even for the most chronic post-stroke patients often relegated from novel interventions, whose expected recovery with conventional therapy is very low. It is necessary to carry out a randomized controlled trial in the future with a larger sample of patients.

Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections

Reported neuro-modulation schemes in the literature are typically classified as closed-loop or open-loop. A novel group of recently developed neuro-modulation devices may be better described as a neural bypass, which attempts to transmit neural data from one location of the nervous system to another. The most common form of neural bypasses in the literature utilize EEG recordings of cortical information paired with functional electrical stimulation for effector muscle output, most commonly for assistive applications and rehabilitation in spinal cord injury or stroke. Other neural bypass locations that have also been described, or may soon be in development, include cortical-spinal bypasses, cortical-cortical bypasses, autonomic bypasses, peripheral-central bypasses, and inter-subject bypasses. The most common recording devices include EEG, ECoG, and microelectrode arrays, while stimulation devices include both invasive and noninvasive electrodes. Several devices are in development to improve the temporal and spatial resolution and biocompatibility for neuronal recording and stimulation. A major barrier to entry includes neuroplasticity and current decoding mechanisms that regularly require retraining. Neural bypasses are a unique class of neuro-modulation. Continued advancement of neural recording and stimulating devices with high spatial and temporal resolution, combined with decoding mechanisms uninhibited by neuroplasticity, can expand the therapeutic capability of neural bypassing. Overall, neural bypasses are a promising modality to improve the treatment of common neurologic disorders, including stroke, spinal cord injury, peripheral nerve injury, brain injury and more.

Use and evaluation of assistive technologies for upper limb function in tetraplegia

CONTEXT

More than half of all spinal cord injuries (SCI) occur at the cervical level leading to loss of upper limb function, restricted activity and reduced independence. Several technologies have been developed to assist with upper limb functions in the SCI population.

OBJECTIVE

There is no clear clinical consensus on the effectiveness of the current assistive technologies for the cervical SCI population, hence this study reviews the literature in the years between 1999 and 2019.

METHODS

A systematic review was performed on the state-of-the-art assistive technology that supports and improves the function of impaired upper limbs in cervical SCI populations. Combinations of terms, covering assistive technology, SCI, and upper limb, were used in the search, which resulted in a total of 1770 articles. Data extractions were performed on the selected studies which involved summarizing details on the assistive technologies, characteristics of study participants, outcome measures, and improved upper limb functions when using the device.

RESULTS

A total of 24 articles were found and grouped into five categories, including neuroprostheses (invasive and non-invasive), orthotic devices, hybrid systems, robots, and arm supports. Only a few selected studies comprehensively reported characteristics of the participants. There was a wide range of outcome measures and all studies reported improvements in upper limb function with the devices.

CONCLUSIONS

This study highlighted that assistive technologies can improve functions of the upper limbs in SCI patients. It was challenging to draw generalizable conclusions because of factors, such as heterogeneity of recruited participants, a wide range of outcome measures, and the different technologies employed.

Effect of Acute Physical Interventions on Pathophysiology and Recovery After Spinal Cord Injury: A Comprehensive Review of the Literature

Physical rehabilitation is essential for enhancing recovery in individuals with spinal cord injury (SCI); however, aside from early surgical intervention and hemodynamic management, there are no proven interventions for promoting recovery in the acute phase. In general, early rehabilitation is considered beneficial, but optimal parameters and potential contraindications for implementing rehabilitation at very early time points are unclear. Moreover, clinical trials to date are limited to studies initiating rehabilitation 2 weeks after injury and later. To address these gaps, this article reviews the preclinical literature on physical interventions initiated within the first 8 days postinjury. Effects of early rehabilitation on molecular and structural nervous system changes, behavioral function, and body systems are considered. Most studies utilized treadmill or cycle training as the primary intervention. Treadmill training initiated within the first 3 days and terminated by 1 week after injury worsened autonomic function, inflammation, and locomotor outcomes, while swim training during this period increased microvascular dysfunction. In contrast, lower-intensity rehabilitation such as reach training, ladder training, or voluntary wheel or ball training showed benefits when implemented during the first 3 days. Rehabilitation initiated at 4 days postinjury was also associated with enhanced motor recovery. Cycling appears to have the greatest risk-benefit ratio; however, the effects of cycle training in the first 3 days were not investigated. Overall, research suggests that lower intensity or voluntary rehabilitation during the hyperacute phase is more appropriate until at least 4 days postinjury, at which point higher-intensity activity becomes safer and more beneficial for recovery.

Slow Firing Single Units Are Essential for Optimal Decoding of Silent Speech

The motivation of someone who is locked-in, that is, paralyzed and mute, is to find relief for their loss of function. The data presented in this report is part of an attempt to restore one of those lost functions, namely, speech. An essential feature of the development of a speech prosthesis is optimal decoding of patterns of recorded neural signals during silent or covert speech, that is, speaking “inside the head” with output that is inaudible due to the paralysis of the articulators. The aim of this paper is to illustrate the importance of both fast and slow single unit firings recorded from an individual with locked-in syndrome and from an intact participant speaking silently. Long duration electrodes were implanted in the motor speech cortex for up to 13 years in the locked-in participant. The data herein provide evidence that slow firing single units are essential for optimal decoding accuracy. Additional evidence indicates that slow firing single units can be conditioned in the locked-in participant 5 years after implantation, further supporting their role in decoding.

Soft robotics and functional electrical stimulation advances for restoring hand function in people with SCI: a narrative review, clinical guidelines and future directions

Background

Recovery of hand function is crucial for the independence of people with spinal cord injury (SCI). Wearable devices based on soft robotics (SR) or functional electrical stimulation (FES) have been employed to assist the recovery of hand function both during activities of daily living (ADLs) and during therapy. However, the implementation of these wearable devices has not been compiled in a review focusing on the functional outcomes they can activate/elicit/stimulate/potentiate. This narrative review aims at providing a guide both for engineers to help in the development of new technologies and for clinicians to serve as clinical guidelines based on the available technology in order to assist and/or recover hand function in people with SCI.

Methods

A literature search was performed in Scopus, Pubmed and IEEE Xplore for articles involving SR devices or FES systems designed for hand therapy or assistance, published since 2010. Only studies that reported functional outcomes from individuals with SCI were selected. The final collections of both groups (SR and FES) were analysed based on the technical aspects and reported functional outcomes.

Results

A total of 37 out of 1101 articles were selected, 12 regarding SR and 25 involving FES devices. Most studies were limited to research prototypes, designed either for assistance or therapy. From an engineering perspective, technological improvements for home-based use such as portability, donning/doffing and the time spent with calibration were identified. From the clinician point of view, the most suitable technical features (e.g., user intent detection) and assessment tools should be determined according to the particular patient condition. A wide range of functional assessment tests were adopted, moreover, most studies used non-standardized tests.

Conclusion

SR and FES wearable devices are promising technologies to support hand function recovery in subjects with SCI. Technical improvements in aspects such as the user intent detection, portability or calibration as well as consistent assessment of functional outcomes were the main identified limitations. These limitations seem to be be preventing the translation into clinical practice of these technological devices created in the laboratory.

Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task

Acquisition of new skills has the potential to disturb existing network function. To directly assess whether previously acquired cortical function is altered during learning, mice were trained in an abstract task in which selected activity patterns were rewarded using an optical brain-computer interface device coupled to primary visual cortex (V1) neurons. Excitatory neurons were longitudinally recorded using 2-photon calcium imaging. Despite significant changes in local neural activity during task performance, tuning properties and stimulus encoding assessed outside of the trained context were not perturbed. Similarly, stimulus tuning was stable in neurons that remained responsive following a different, visual discrimination training task. However, visual discrimination training increased the rate of representational drift. Our results indicate that while some forms of perceptual learning may modify the contribution of individual neurons to stimulus encoding, new skill learning is not inherently disruptive to the quality of stimulus representation in adult V1.

Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals

For brain–computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.

The role of electrical stimulation for rehabilitation and regeneration after spinal cord injury

Electrical stimulation is used to elicit muscle contraction and can be utilized for neurorehabilitation following spinal cord injury when paired with voluntary motor training. This technology is now an important therapeutic intervention that results in improvement in motor function in patients with spinal cord injuries. The purpose of this review is to summarize the various forms of electrical stimulation technology that exist and their applications. Furthermore, this paper addresses the potential future of the technology.

Exploring Cognition with Brain-Machine Interfaces

Traditional brain-machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.

Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates

[Figure: see text].

Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications

Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.

Generalizable cursor click decoding using grasp-related neural transients

Objective.Intracortical brain-computer interfaces (iBCI) have the potential to restore independence for individuals with significant motor or communication impairments. One of the most realistic avenues for clinical translation of iBCI technology is enabling control of a computer cursor-i.e. movement-related neural activity is interpreted (decoded) and used to drive cursor function. Here we aim to improve cursor click decoding to allow for both point-and-click and click-and-drag control.Approach.Using chronic microelectrode arrays implanted in the motor cortex of two participants with tetraplegia, we identified prominent neural responses related to attempted hand grasp. We then developed a new approach for decoding cursor click (hand grasp) based on the most salient responses.Main results.We found that the population-wide response contained three dominant components related to hand grasp: an onset transient response, a sustained response, and an offset transient response. The transient responses were larger in magnitude-and thus more reliably detected-than the sustained response, and a click decoder based on these transients outperformed the standard approach of binary state classification.Significance.A transient-based approach for identifying hand grasp can provide a high degree of cursor click control for both point-and-click and click-and-drag applications. This generalized click functionality is an important step toward high-performance cursor control and eventual clinical translation of iBCI technology.

Long-term intracortical microelectrode array performance in a human: a 5 year retrospective analysis

Objective. Brain-computer interfaces (BCIs) that record neural activity using intracortical microelectrode arrays (MEAs) have shown promise for mitigating disability associated with neurological injuries and disorders. While the chronic performance and failure modes of MEAs have been well studied and systematically described in non-human primates, there is far less reported about long-term MEA performance in humans. Our group has collected one of the largest neural recording datasets from a Utah MEA in a human subject, spanning over 5 years (2014-2019). Here we present both long-term signal quality and BCI performance as well as highlight several acute signal disruption events observed during the clinical study.Approach. Long-term Utah array performance was evaluated by analyzing neural signal metric trends and decoding accuracy for tasks regularly performed across 448 clinical recording sessions. For acute signal disruptions, we identify or hypothesize the root cause of the disruption, show how the disruption manifests in the collected data, and discuss potential identification and mitigation strategies for the disruption.Main results. Neural signal quality metrics deteriorated rapidly within the first year, followed by a slower decline through the remainder of the study. Nevertheless, BCI performance remained high 5 years after implantation, which is encouraging for the translational potential of this technology as an assistive device. We also present examples of unanticipated signal disruptions during chronic MEA use, which are critical to detect as BCI technology progresses toward home usage.Significance. Our work fills a gap in knowledge around long-term MEA performance in humans, providing longevity and efficacy data points to help characterize the performance of implantable neural sensors in a human population. The trial was registered on ClinicalTrials.gov (Identifier NCT01997125) and conformed to institutional requirements for the conduct of human subjects research.

Advances in motion and electromyography based wearable technology for upper extremity function rehabilitation: A review

STUDY DESIGN

Scoping review.

INTRODUCTION

With the recent advances in technologies, interactive wearable technologies including inertial motion sensors and e-textiles are emerging in the field of rehabilitation to monitor and provide feedback and therapy remotely.

PURPOSE OF THE STUDY

This review article focuses on inertial measurement unit motion sensor and e-textiles-based technologies and proposes approaches to augment these interactive wearable technologies.

METHODS

We conducted a comprehensive search of relevant electronic databases (eg, PubMed, the Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, The Cochrane Central Register of Controlled Trial, and the Physiotherapy Evidence Database). The scoping review included all study designs.

RESULTS

Currently, there are a numerous research groups and companies investigating inertial motion sensors and e-textiles-based interactive wearable technologies. However, translation of these technologies to the clinic would need further research to increase ease of use and improve clinical validity of the outcomes of these technologies.

DISCUSSION

The current review discusses the limitations of the interactive wearable technologies such as, limited clinical utility, bulky equipment, difficulty in setting up equipment inertial motion sensors and e-textiles.

CONCLUSION

There is tremendous potential for interactive wearable technologies in rehabilitation. With the evolution of cloud computing, interactive wearable systems can remotely provide intervention and monitor patient progress using models of telerehabilitation. This will revolutionize the delivery of rehabilitation and make rehabilitation more accessible and affordable to millions of individuals.

Auditory cues reveal intended movement information in middle frontal gyrus neuronal ensemble activity of a person with tetraplegia

Intracortical brain-computer interfaces (iBCIs) allow people with paralysis to directly control assistive devices using neural activity associated with the intent to move. Realizing the full potential of iBCIs critically depends on continued progress in understanding how different cortical areas contribute to movement control. Here we present the first comparison between neuronal ensemble recordings from the left middle frontal gyrus (MFG) and precentral gyrus (PCG) of a person with tetraplegia using an iBCI. As expected, PCG was more engaged in selecting and generating intended movements than in earlier perceptual stages of action planning. By contrast, MFG displayed movement-related information during the sensorimotor processing steps preceding the appearance of the action plan in PCG, but only when the actions were instructed using auditory cues. These results describe a previously unreported function for neurons in the human left MFG in auditory processing contributing to motor control.

Neurorestoration: Advances in human brain–computer interface using microelectrode arrays

Neural damage has been a great challenge to the medical field for a very long time. The emergence of brain–computer interfaces (BCIs) offered a new possibility to enhance the activity of daily living and provide a new formation of entertainment for those with disabilities. Intracortical BCIs, which require the implantation of microelectrodes, can receive neuronal signals with a high spatial and temporal resolution from the individual’s cortex. When BCI decoded cortical signals and mapped them to external devices, it displayed the ability not only to replace part of the human motor function but also to help individuals restore certain neurological functions. In this review, we focus on human intracortical BCI research using microelectrode arrays and summarize the main directions and the latest results in this field. In general, we found that intracortical BCI research based on motor neuroprosthetics and functional electrical stimulation have already achieved some simple functional replacement and treatment of motor function. Pioneering work in the posterior parietal cortex has given us a glimpse of the potential that intracortical BCIs have to control external devices and receive various sensory information.

Restoring Movement in Paralysis with a Bioelectronic Neural Bypass Approach: Current State and Future Directions

Bioelectronic medicine is a rapidly growing field that explores targeted neuromodulation in new treatment options addressing both disease and injury. New bioelectronic methods are being developed to monitor and modulate neural activity directly. The therapeutic benefit of these approaches has been validated in recent clinical studies in various conditions, including paralysis. By using decoding and modulation strategies together, it is possible to restore lost function to those living with paralysis and other debilitating conditions by interpreting and rerouting signals around the affected portion of the nervous system. This, in effect, creates a bioelectronic "neural bypass" to serve the function of the damaged/degenerated network. By learning the language of neurons and using neural interface technology to tap into critical networks, new approaches to repairing or restoring function in areas impacted by disease or injury may become a reality.

Closed-loop neuromuscular electrical stimulation using feedforward-feedback control and textile electrodes to regulate grasp force in quadriplegia

BACKGROUND

Transcutaneous neuromuscular electrical stimulation is routinely used in physical rehabilitation and more recently in brain-computer interface applications for restoring movement in paralyzed limbs. Due to variable muscle responses to repeated or sustained stimulation, grasp force levels can change significantly over time. Here we develop and assess closed-loop methods to regulate individual finger forces to facilitate functional movement. We combined this approach with custom textile-based electrodes to form a light-weight, wearable device and evaluated in paralyzed study participants.

METHODS

A textile-based electrode sleeve was developed by the study team and Myant, Corp. (Toronto, ON, Canada) and evaluated in a study involving three able-body participants and two participants with quadriplegia. A feedforward-feedback control structure was designed and implemented to accurately maintain finger force levels in a quadriplegic study participant.

RESULTS

Individual finger flexion and extension movements, along with functional grasping, were evoked during neuromuscular electrical stimulation. Closed-loop control methods allowed accurate steady state performance (< 15% error) with a settling time of 0.67 s (SD = 0.42 s) for individual finger contact force in a participant with quadriplegia.

CONCLUSIONS

Textile-based electrodes were identified to be a feasible alternative to conventional electrodes and facilitated individual finger movement and functional grasping. Furthermore, closed-loop methods demonstrated accurate control of individual finger flexion force. This approach may be a viable solution for enabling grasp force regulation in quadriplegia.

TRIAL REGISTRATION

NCT, NCT03385005. Registered Dec. 28, 2017.

Brain Computer Interfaces in Rehabilitation Medicine

One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.

Human motor decoding from neural signals: a review

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

Clinical neuroprosthetics: Today and tomorrow

Implantable neurostimulation devices provide a direct therapeutic link to the nervous system and can be considered brain-computer interfaces (BCI). Under this definition, BCI are not simply science fiction, they are part of existing neurosurgical practice. Clinical BCI are standard of care for historically difficult to treat neurological disorders. These systems target the central and peripheral nervous system and include Vagus Nerve Stimulation, Responsive Neurostimulation, and Deep Brain Stimulation. Recent advances in clinical BCI have focused on creating "closed-loop" systems. These systems rely on biomarker feedback and promise individualized therapy with optimal stimulation delivery and minimal side effects. Success of clinical BCI has paralleled research efforts to create BCI that restore upper extremity motor and sensory function to patients. Efforts to develop closed loop motor/sensory BCI is linked to the successes of today's clinical BCI.

Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation

OBJECTIVE

To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic.

DESIGN

Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days 137 to 1478.

SETTING

Tertiary care outpatient rehabilitation center.

PARTICIPANT

A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injury INTERVENTIONS: After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordinated forearm, wrist, and hand movements.

MAIN OUTCOME MEASURES

Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, and Prehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functional activity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Independence Measure-Self-Report [SCIM-SR]) with and without the BCI-FES.

RESULTS

With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can, fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-T wrist and hand skills. QIF-SF and SCIM-SR eating, grooming, and toileting activities were expected to improve with home use of BCI-FES. Pincer grips and mobility were unaffected. BCI-FES grip skills enabled the participant to play an adapted "Battleship" game and manipulate household objects.

CONCLUSIONS

Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upper limb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologic level gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.

Opportunities and challenges for developing closed-loop bioelectronic medicines

The peripheral nervous system plays a major role in the maintenance of our physiology. Several peripheral nerves intimately regulate the state of the brain, spinal cord, and visceral systems. A new class of therapeutics, called bioelectronic medicines, are being developed to precisely regulate physiology and treat dysfunction using peripheral nerve stimulation. In this review, we first discuss new work using closed-loop bioelectronic medicine to treat upper limb paralysis. In contrast to open-loop bioelectronic medicines, closed-loop approaches trigger ‘on demand’ peripheral nerve stimulation due to a change in function (e.g., during an upper limb movement or a change in cardiopulmonary state). We also outline our perspective on timing rules for closed-loop bioelectronic stimulation, interface features for non-invasively stimulating peripheral nerves, and machine learning algorithms to recognize disease events for closed-loop stimulation control. Although there will be several challenges for this emerging field, we look forward to future bioelectronic medicines that can autonomously sense changes in the body, to provide closed-loop peripheral nerve stimulation and treat disease.

Selective Recruitment of Arm Motoneurons in Nonhuman Primates Using Epidural Electrical Stimulation of the Cervical Spinal Cord

Recovery of reaching and grasping ability is the priority for people with cervical spinal cord injury (SCI). Epidural electrical stimulation (EES) has shown promising results in improving motor control after SCI in various animal models and in humans. Notably, the application of stimulation bursts with spatiotemporal sequences that reproduce the natural activation of motoneurons restored skilled leg movements in rodent and nonhuman primate models of SCI. Here, we studied whether this conceptual framework could be transferred to the design of cervical EES protocols for the recovery of reaching and grasping in nonhuman primates. We recorded muscle activity during a reaching and grasping task in a macaque monkey and found that this task involves a stereotypical spatiotemporal map of motoneuron activation. We then characterized the specificity of a spinal implant for the delivery of EES to cervical spinal segments in the same animal. Finally, we combined these results to design a simple stimulation protocol that may reproduce natural motoneuron activation and thus facilitate upper limb movements after injury.

A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent

Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his performance to that of able-bodied participants performing the same task, establishing a quantitative target for ideal BCI-FES performance on this task. Cumulatively, these results help quantify BCI decoder performance characteristics relevant to potential users and the complex interactions between them.

Meeting brain-computer interface user performance expectations using a deep neural network decoding framework

Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices^1-9. Surveys of potential end-users have identified key BCI system features^10-14, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm^1,15, which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network¹⁶ decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracortical data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure^3,17-20, responds faster than competing methods⁸, and can increase functionality with minimal retraining by using a technique known as transfer learning²¹. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT)²². These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology.

A High Definition Noninvasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human With Tetraplegia

OBJECTIVE

Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality.

METHODS

The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a noninvasive neuromuscular electrical stimulation (NMES) system.

RESULTS

We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task.

CONCLUSION

This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement.

SIGNIFICANCE

These results have implications for enabling complex rotary hand functions in sequence with other functionally relevant movements for patients suffering from SCI, stroke, and other sensorimotor dysfunctions.

Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications

Background

Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.

Methods

In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.

Results

All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.

Conclusions

Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications.

Trial registration

This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125).

Electronic supplementary material

The online version of this article (10.1186/s42234-018-0011-x) contains supplementary material, which is available to authorized users.

Unique Sensory and Motor Behavior in Thy1-GFP-M Mice before and after Spinal Cord Injury

Sensorimotor recovery after spinal cord injury (SCI) is of utmost importance to injured individuals and will rely on improved understanding of SCI pathology and recovery. Novel transgenic mouse lines facilitate discovery, but must be understood to be effective. The purpose of this study was to characterize the sensory and motor behavior of a common transgenic mouse line (Thy1-GFP-M) before and after SCI. Thy1-GFP-M positive (TG+) mice and their transgene negative littermates (TG-) were acquired from two sources (in-house colony, n = 32, Jackson Laboratories, n = 4). C57BL/6J wild-type (WT) mice (Jackson Laboratories, n = 10) were strain controls. Moderate-severe T9 contusion (SCI) or transection (TX) occurred in TG+ (SCI, n = 25, TX, n = 5), TG- (SCI, n = 5), and WT (SCI, n = 10) mice. To determine responsiveness to rehabilitation, a cohort of TG+ mice with SCI (n = 4) had flat treadmill (TM) training 42-49 days post-injury (dpi). To characterize recovery, we performed Basso Mouse Scale, Grid Walk, von Frey Hair, and Plantar Heat Testing before and out to day 42 post-SCI. Open field locomotion was significantly better in the Thy1 SCI groups (TG+ and TG-) compared with WT by 7 dpi (p < 0.01) and was maintained through 42 dpi (p < 0.01). These unexpected locomotor gains were not apparent during grid walking, indicating severe impairment of precise motor control. Thy1 derived mice were hypersensitive to mechanical stimuli at baseline (p < 0.05). After SCI, mechanical hyposensitivity emerged in Thy1 derived groups (p < 0.001), while thermal hyperalgesia occurred in all groups (p < 0.001). Importantly, consistent findings across TG+ and TG- groups suggest that the effects are mediated by the genetic background rather than transgene manipulation itself. Surprisingly, TM training restored mechanical and thermal sensation to baseline levels in TG+ mice with SCI. This behavioral profile and responsiveness to chronic training will be important to consider when choosing models to study the mechanisms underlying sensorimotor recovery after SCI.

Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.

Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia

Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with >95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.

Motor cortical activity changes during neuroprosthetic-controlled object interaction

Brain-computer interface (BCI) controlled prosthetic arms are being developed to restore function to people with upper-limb paralysis. This work provides an opportunity to analyze human cortical activity during complex tasks. Previously we observed that BCI control became more difficult during interactions with objects, although we did not quantify the neural origins of this phenomena. Here, we investigated how motor cortical activity changed in the presence of an object independently of the kinematics that were being generated using intracortical recordings from two people with tetraplegia. After identifying a population-wide increase in neural firing rates that corresponded with the hand being near an object, we developed an online scaling feature in the BCI system that operated without knowledge of the task. Online scaling increased the ability of two subjects to control the robotic arm when reaching to grasp and transport objects. This work suggests that neural representations of the environment, in this case the presence of an object, are strongly and consistently represented in motor cortex but can be accounted for to improve BCI performance.