Locomotor learning in infants at high risk for cerebral palsy: A study protocol.

Background: Physical disability in individuals with cerebral palsy (CP) creates lifelong mobility challenges and healthcare costs. Despite this, very little is known about how infants at high risk for CP learn to move and acquire early locomotor skills, which set the foundation for lifelong mobility. The objective of this project is to characterize the evolution of locomotor learning over the first 18 months of life in infants at high risk for CP. To characterize how locomotor skill is learned, we will use robotic and sensor technology to provide intervention and longitudinally study infant movement across three stages of the development of human motor control: early spontaneous movement, prone locomotion (crawling), and upright locomotion (walking). Study design: This longitudinal observational/intervention cohort study (ClinicalTrials.gov Identifier: NCT04561232) will enroll sixty participants who are at risk for CP due to a brain injury by one month post-term age. Study participation will be completed by 18 months of age. Early spontaneous leg movements will be measured monthly from 1 to 4 months of age using inertial sensors worn on the ankles for two full days each month. Infants who remain at high risk for CP at 4 months of age, as determined from clinical assessments of motor function and movement quality, will continue through two locomotor training phases. Prone locomotor training will be delivered from 5 to 9 months of age using a robotic crawl training device that responds to infant behavior in real-time. Upright locomotor training will be delivered from 9 to 18 months of age using a dynamic weight support system to allow participants to practice skills beyond their current level of function. Repeated assessments of locomotor skill, training characteristics (such as movement error, variability, movement time and postural control), and variables that may mediate locomotor learning will be collected every two months during prone training and every three months during upright training. Discussion: This study will develop predictive models of locomotor skill acquisition over time. We hypothesize that experiencing and correcting movement errors is critical to skill acquisition in infants at risk for CP and that locomotor learning is mediated by neurobehavioral factors outside of training.Project Number 1R01HD098364-01A1.ClinicalTrials.gov Identifier: NCT04561232.

Comparing Temporospatial Performance During Brisk and Self-Paced Walking by Men With Osteomyoplastic Transfemoral Amputation and Controls Using Pressure and Muscle Activation Peak Times.

Background: The aim of osteomyoplastic transfemoral amputation (OTFA) is to produce sustained, robust prosthetic gait performance by residuum reconstructing. A better understanding of residuum-socket interface pressures (RSI) and residuum muscle activation should uniquely reveal gait stability to better inform long-term rehabilitation goals. Objectives: The objectives of this study are to characterize RSI pressures and residuum muscle activation in men with OTFA while walking at two speeds and compare temporospatial muscle activation with intact controls. Methods: In this study, we observed and compared healthy men with OTFA and controls during 2-min gait trials at brisk and self-paced speeds, two visits, and 1 year apart. RSI pressures and hip adductors, hamstrings, and quadriceps activation were recorded for those with OTFA. OTFA temporospatial muscle activation patterns were compared with the controls. Within the extracted strides, heel-strike and toe-off events and EMG activation peak times were characterized and compared. Peak times for pressure and EMG activity were examined in individual muscles and antagonist muscles of residual and intact limbs. Results: Six men with OTFA exhibited adductor, hamstring, and quadriceps co-contraction within intact and residual limbs, regardless of walking speed or trial. Co-contraction within their intact limb occurred throughout the gait cycle. Within the residuum, co-contraction occurred during weight transference. The 75% most likely RSI peaks occurred during stance. EMG peaks were 75% most likely to occur during early stance, terminal stance-initial swing, and terminal swing. Conclusion: Participants with OTFA demonstrated adductors-hamstrings-quadriceps co-contraction in the intact thigh and residuum with corresponding RSI pressure increase, primarily during transitions between stance and swing, indicating gait instability, demonstrating the need to explicitly address these deficits continuously in rehabilitation and wellness settings.

Applications of Biomechanical Foot Models to Evaluate Dance Movements Using Three-Dimensional Motion Capture: A Review of the Literature.

Dance movement requires excessive, repetitive range of motion (ROM) at the foot-ankle complex, possibly contributing to the high rate of injury among dancers. However, we know little about foot biomechanics during dance movements. Researchers are using three-dimensional (3D) motion capture systems to study the in vivo kinematics of joint segments more frequently in dance-medicine research, warranting a literature review and quality assessment evaluation. The purpose of this literature review was to identify and evaluate studies that used 3D motion capture to analyze in vivo biomechanics of the foot and ankle for a cohort of dancers during dance-specific movement. Three databases (PubMed, Ovid MEDLINE, CINAHL) were accessed along with hand searches of dance-specific journals to identify relevant articles through March 2020. Using specific selection criteria, 25 studies were identified. Fifteen studies used single-segment biomechanical foot models originally created to study gait, four used a novel two-segment model, and six utilized a multi-seg- ment foot model. Nine of the studies referenced common and frequently published gait marker sets and four used a dance-specific biomechanical model with purposefully designed foot segments to analyze the dancers' foot and ankle. Description of the biomechanical models varied, reducing the reproducibility of the models and protocols. Investigators concluded that there is little evidence that the extreme total, segmental, and inter-segmental foot and ankle ROM exerted by dancers are being evaluated during dance-specific movements using 3D motion capture. Findings suggest that 3D motion capture is a robust measurement tool that has the capability to assist researchers in evaluating the in vivo, inter-segmental motion of the foot and ankle to potentially discover many of the remaining significant factors predisposing dancers to injury. The literature review synthesis is presented with recommendations for consideration when evaluating results from studies that utilized a 3D biomechanical foot model to evaluate dance-specific movement.

The Impact of Cognition on Motor Learning and Skill Acquisition Using a Robot Intervention in Infants With Cerebral Palsy.

Background: Cerebral Palsy (CP) is a neurodevelopmental disorder that encompasses multiple neurological disorders that appear in infancy or early childhood and persist through the lifespan of the individual. Early interventions for infants with CP utilizing assisted-motion robotic devices have shown promising effects in rehabilitation of the motor function skills. The impact of cognitive function during motor learning and skill acquisition in infants using robotic technologies is unclear. Purpose: To assess the impact of cognitive function of infants with and without CP on their motor learning using the Self-Initiated Prone Progression Crawler (SIPPC) robot. Methods: Statistical analysis was conducted on the data obtained from a randomized control trial in which the movement learning strategies in infants with or at risk for CP was assessed during a 16-week SIPPC robot intervention. Cognitive function was measured by the Bayley scales of Infant and Toddler Development-Third edition (Bayley-III) and motor function was measured by the Movement Observation Coding Scheme (MOCS). The infants were categorized into three distinct groups based on their cognitive scores at baseline: "above average" (n1 = 11), "below average" (n2 = 10), and "average" (n3 = 26). Tri-weekly averages of the MOCS scores (observations at five time points) were used for the analyses. This study involved computing descriptive statistics, data visualization, repeated measures analysis of variances (rmANOVA), and survival analyses. Results: The descriptive statistics were calculated for the MOCS and Bayley III scores. The repeated measures ANOVAs revealed that there was a statistically significant effect of time (p < 0.0001) on scores of all subscales of the MOCS. A statistically significant effect of interaction between group and time (p < 0.05) was found in MOCS scores of subscales 1 and 2. The survival analyses indicated that infants in different cognition groups significantly differed (p < 0.0001) in their ability to achieve the crawling milestone within the 16-week intervention period. Conclusion: The findings in this study reveal the key movement strategies required to move the SIPPC robot, assessed by the MOCS, vary depending on the infants' cognition. The SIPPC robot is well-matched to cognitive ability of infants with CP. However, lower cognitive ability was related to delayed improvement in their motor skills.

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.

Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the current applications of machine learning in cervical and lumbar degenerative spine disease. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of PubMed, Embase, Medline, and Cochrane was conducted through May 31st, 2020, using the following terms: "artificial intelligence" OR "machine learning" AND "neurosurgery" AND "spine." Studies were included if original research on machine learning was utilized in patient care for degenerative spine disease, including radiographic machine learning applications. Studies focusing on robotic applications in neurosurgery, navigation, or stereotactic radiosurgery were excluded. The literature search identified 296 papers, with 35 articles meeting inclusion criteria. There were nine studies involving cervical degenerative spine disease and 26 studies on lumbar degenerative spine disease. The majority of studies for both cervical and lumbar spines utilized machine learning for the prediction of postoperative outcomes, with 5 (55.6%) and 15 (61.5%) studies, respectively. Machine learning applications focusing on degenerative lumbar spine greatly outnumber the current volume of cervical spine studies. The current research in lumbar spine also demonstrates more advanced clinical applications of radiographic, diagnostic, and predictive machine learning models.

Robot Reinforcement and Error-Based Movement Learning in Infants With and Without Cerebral Palsy.

BACKGROUND: Prone mobility, central to development of diverse psychological and social processes that have lasting effects on life participation, is seldom attained by infants with cerebral palsy (CP) and has no tested interventions. Reinforcement learning (RL) and error-based movement learning (EBL) offer novel intervention possibilities. OBJECTIVE: This study examined movement learning strategies in infants with or at risk for CP using RL and EBL during acquisition of prone locomotion. DESIGN: The study was a randomized trial that used repeated measures. SETTING: The study setting was a university physical therapy clinic in the United States. PATIENTS: Thirty infants aged 4.5 to 6.5 months participated in the study: 24 had or were at risk for CP, and 6 were typically developing. INTERVENTION: Infants with and at risk for CP were randomly assigned to a combination of RL and EBL (SIPPC-RE), or RL only (SIPPC-R) conditions. Infants with typical development comprised the RL-only reference group (SIPPC-TD). Infants trained in prone locomotion with the Self-Initiated Prone Progression Crawler (SIPPC) robotic system for three 5-minute trials, twice a week for 12 weeks in their homes or child care. All training sessions were videotaped for behavioral coding. MEASUREMENTS: The SIPPC gathered robot and infant trunk/limb movement data. Randomized 2-way analysis of variance with repeated measures and Pearson r to analyze the data was used. RESULTS: Results included the number of arm movements and trial-and-error activity distinguished between the SIPPC-RE and SIPPC-R groups. The mean change in arm movements from baseline for the SIPPC-RE and SIPPC-R groups was 4.8 m and -7.0 m, respectively. The mean differences in rotational amplitude (trial and error) from baseline to the end of the study were 278 degrees and 501 degrees, respectively. These changes were correlated with distance traveled and goal-directed movements. The latter increased over the 12 weeks for the SIPPC-RE and SIPPC-TD groups, but not the SIPPC-R group. LIMITATIONS: The CP groups were unequal due to reassignment and did not include a typically developing comparison group of a combination of RL and EBL. CONCLUSION: These findings suggest movement learning and retention in infants with CP is differentially affected by the use of RL and EBL, with a combination of both showing more promise than RL alone. The findings also implicate cognition, type of brain insult, emergence of reaching, and muscle force production, which must be explored in future studies.

Changes in cortical network connectivity with long-term brain-machine interface exposure after chronic amputation.

Studies on neural plasticity associated with brain-machine interface (BMI) exposure have primarily documented changes in single neuron activity, and largely in intact subjects. Here, we demonstrate significant changes in ensemble-level functional connectivity among primary motor cortical (MI) neurons of chronically amputated monkeys exposed to control a multiple-degree-of-freedom robot arm. A multi-electrode array was implanted in M1 contralateral or ipsilateral to the amputation in three animals. Two clusters of stably recorded neurons were arbitrarily assigned to control reach and grasp movements, respectively. With exposure, network density increased in a nearly monotonic fashion in the contralateral monkeys, whereas the ipsilateral monkey pruned the existing network before re-forming a denser connectivity. Excitatory connections among neurons within a cluster were denser, whereas inhibitory connections were denser among neurons across the two clusters. These results indicate that cortical network connectivity can be modified with BMI learning, even among neurons that have been chronically de-efferented and de-afferented due to amputation.

Characterization of infant mu rhythm immediately before crawling: A high-resolution EEG study.

Crawling is an important milestone in infant motor development. However, infants with developmental motor disorders can exhibit delays, or even miss, in the acquisition of crawling skill. And little information is available from the neurodevelopmental domain about the changes in brain function with intervention. The mu rhythm can potentially play a substantial role in understanding human motor development at early ages in infants, as it has in adults. Studies about the mu rhythm in infants were in coarse temporal resolution with longitudinal samples taken months or years apart. Details about the infant mu rhythm at a fine age resolution has not been fully revealed, which leads to contradictory evidence about its formulation and developmental changes of its spectral origins and, therefore, impedes the full understanding of motor brain development before crawling skill acquisition. The present study aims to expand knowledge about the infant mu rhythm and its spatio-spectral pattern shifts along maturation immediately before crawling. With high-density EEG data recorded on a weekly basis and simultaneous characterization of spatio-spectral patterns of the mu rhythm, subtle developmental changes in its spectral peak, frequency range, and scalp topography are revealed. This mu rhythm further indicates a significant correlation to the crawling onset while powers from other frequency bands do not show such correlations. These details of developmental changes about the mu rhythm provide an insight of rapid changes in the human motor cortex in the first year of life. Our results are consistent with previous findings about the peak frequency shifting of the mu rhythm and further depict detailed developmental curves of its frequency ranges and spatial topographies. The infant mu rhythm could potentially be used to assess motor brain deficiencies at early ages and to evaluate intervention effectiveness in children with neuromotor disorders.

Spectra of infant EEG within the first year of life: A pilot study.

Rhythmic activities in electroencephalography (EEG) have been extensively studied in adults and classic rhythms are found to correlate with specific human brain functions. However, less has been investigated in infant EEG, and EEG rhythms in infants at early ages have not been well characterized in terms of their frequency ranges. In the present pilot study, we investigated rhythmic activities in infant EEG recorded weekly from 4-8 months using high-density EEG sensor nets. The developmental changes of EEG rhythms in different frequency bands along maturation were evaluated through spectral analysis. Their longitudinal scalp maps were also studied to understand their plausible functional correlates. The present study aims to enrich the sparse knowledge about the developing patterns of EEG rhythms within the first year of life from EEG recordings of high temporal and spatial resolutions.

Tracking single units in chronic, large scale, neural recordings for brain machine interface applications.

In the study of population coding in neurobiological systems, tracking unit identity may be critical to assess possible changes in the coding properties of neuronal constituents over prolonged periods of time. Ensuring unit stability is even more critical for reliable neural decoding of motor variables in intra-cortically controlled brain-machine interfaces (BMIs). Variability in intrinsic spike patterns, tuning characteristics, and single-unit identity over chronic use is a major challenge to maintaining this stability, requiring frequent daily calibration of neural decoders in BMI sessions by an experienced human operator. Here, we report on a unit-stability tracking algorithm that efficiently and autonomously identifies putative single-units that are stable across many sessions using a relatively short duration recording interval at the start of each session. The algorithm first builds a database of features extracted from units' average spike waveforms and firing patterns across many days of recording. It then uses these features to decide whether spike occurrences on the same channel on one day belong to the same unit recorded on another day or not. We assessed the overall performance of the algorithm for different choices of features and classifiers trained using human expert judgment, and quantified it as a function of accuracy and execution time. Overall, we found a trade-off between accuracy and execution time with increasing data volumes from chronically implanted rhesus macaques, with an average of 12 s processing time per channel at ~90% classification accuracy. Furthermore, 77% of the resulting putative single-units matched those tracked by human experts. These results demonstrate that over the span of a few months of recordings, automated unit tracking can be performed with high accuracy and used to streamline the calibration phase during BMI sessions. Our findings may be useful to the study of population coding during learning, and to improve the reliability of BMI systems and accelerate their deployment in clinical applications.

Differences in motor cortical representations of kinematic variables between action observation and action execution and implications for brain-machine interfaces.

Observing an action being performed and executing the same action cause similar patterns of neural activity to emerge in the primary motor cortex (MI). Previous work has shown that the neural activity evoked during action observation (AO) is informative as to both the kinematics and muscle activation patterns of the action being performed, although the neural activity recorded during action observation contains less information than the activity recorded during action execution (AE). In this study, we extend these results by comparing the representation of different kinematic variables in MI single /multi unit activity between AO and AE conditions in three rhesus macaques. We show that the representation of acceleration decreases more significantly than that of position and velocity in AO (population decoding performance for acceleration decreases more steeply, and fewer neurons in AO encode acceleration significantly as compared to AE). We discuss the relevance of these results to brain-machine interfaces that make use of neural activity during AO to initialize a mapping function between neural activity and motor commands.

Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation.

Operant conditioning with biofeedback has been shown to be an effective method to modify neural activity to generate goal-directed actions in a brain-machine interface. It is particularly useful when neural activity cannot be mathematically mapped to motor actions of the actual body such as in the case of amputation. Here, we implement an operant conditioning approach with visual feedback in which an amputated monkey is trained to control a multiple degree-of-freedom robot to perform a reach-to-grasp behavior. A key innovation is that each controlled dimension represents a behaviorally relevant synergy among a set of joint degrees-of-freedom. We present a number of behavioral metrics by which to assess improvements in BMI control with exposure to the system. The use of non-human primates with chronic amputation is arguably the most clinically-relevant model of human amputation that could have direct implications for developing a neural prosthesis to treat humans with missing upper limbs.

Online adaptive decoding of intended movements with a hybrid kinetic and kinematic brain machine interface.

Traditional brain machine interfaces for control of a prosthesis have typically focused on the kinematics of movement, rather than the dynamics. BMI decoders that extract the forces and/or torques to be applied by a prosthesis have the potential for giving the patient a much richer level of control across different dynamic scenarios or even scenarios in which the dynamics of the limb/environment are changing. However, it is a challenge to train a decoder that is able to capture this richness given the small amount of calibration data that is usually feasible to collect a priori. In this work, we propose that kinetic decoders should be continuously calibrated based on how they are used by the subject. Both intended hand position and joint torques are decoded simultaneously as a monkey performs a random target pursuit task. The deviation between intended and actual hand position is used as an estimate of error in the recently decoded joint torques. In turn, these errors are used to drive a gradient descent algorithm for improving the torque decoder parameters. We show that this approach is able to quickly restore the functionality of a torque decoder following substantial corruption with Gaussian noise.

A dual process account of coarticulation in motor skill acquisition.

Many tasks, such as typing a password, are decomposed into a sequence of subtasks that can be accomplished in many ways. Behavior that accomplishes subtasks in ways that are influenced by the overall task is often described as "skilled" and exhibits coarticulation. Many accounts of coarticulation use search methods that are informed by representations of objectives that define skilled. While they aid in describing the strategies the nervous system may follow, they are computationally complex and may be difficult to attribute to brain structures. Here, the authors present a biologically- inspired account whereby skilled behavior is developed through 2 simple processes: (a) a corrective process that ensures that each subtask is accomplished, but does not do so skillfully and (b) a reinforcement learning process that finds better movements using trial and error search that is not informed by representations of any objectives. We implement our account as a computational model controlling a simulated two-armed kinematic "robot" that must hit a sequence of goals with its hands. Behavior displays coarticulation in terms of which hand was chosen, how the corresponding arm was used, and how the other arm was used, suggesting that the account can participate in the development of skilled behavior.

Improving brain-machine interface performance by decoding intended future movements.

OBJECTIVE: A brain-machine interface (BMI) records neural signals in real time from a subject's brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject's intended movements a short time lead in the future. APPROACH: We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user's intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. MAIN RESULTS: Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user's future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. SIGNIFICANCE: This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user's future intent can compensate for the negative effect of control delay on BMI performance.

Compensating for delays in brain-machine interfaces by decoding intended future movement.

Typically, brain-machine interfaces that enable the control of a prosthetic arm work by decoding a subjects' intended hand position or velocity and using a controller to move the arm accordingly. Researchers taking this approach often choose to decode the subjects' desired arm state in the present moment, which causes the prosthetic arm to lag behind the state desired by the user, as the dynamics of the arm (and other control delays) constrain how quickly the controller can change the arm's state. We tested the hypothesis that decoding the subjects' intended future movements would mitigate this lag and improve BMI performance. Offline results show that predictions of future movement (≤ 200 ms) can be made with essentially the same accuracy as predictions of present movement. Online results from one monkey show that performance increases as a function of the future prediction time lead, reaching optimum performance at a time lead equal to the delay inherent in the controlled system.

Continuous decoding of intended movements with a hybrid kinetic and kinematic brain machine interface.

Although most brain-machine interface (BMI) studies have focused on decoding kinematic parameters of motion, it is known that motor cortical activity also correlates with kinetic signals, including hand force and joint torque. In this experiment, a monkey used a cortically-controlled BMI to move a visual cursor and hit a sequence of randomly placed targets. By varying the contributions of separate kinetic and kinematic decoders to the movement of a virtual arm, we evaluated the hypothesis that a BMI incorporating both signals (Hybrid BMI) would outperform a BMI decoding kinematic information alone (Position BMI). We show that the trajectories generated by the Hybrid BMI during real-time decoding were straighter and smoother than those of the Position BMI. These results may have important implications for BMI applications that require controlling devices with inherent, physical dynamics or applying forces to the environment.

Incorporating feedback from multiple sensory modalities enhances brain-machine interface control.

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain-machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey's arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback.

Toward a biomimetic, bidirectional, brain machine interface.

The interest in Brain Machine Interface (BMI) systems has increased tremendously in recent times; many groups have become involved in this type of research, and progress has been quite encouraging. However, two fundamental limitations remain: 1) With a few notable exceptions, BMIs extract only kinematic information from the brain, ignoring the wealth of force or kinetic information also present in the primary motor cortex, and 2) most existing BMIs depend exclusively on natural vision to guide movement, lacking the rapid proprioceptive feedback that is critical for normal movement. The work reported here describes our efforts to address both of these limitations.

Kinetic trajectory decoding using motor cortical ensembles.

Although most brain-machine interface (BMI) studies have focused on decoding kinematic parameters of motion such as hand position and velocity, it is known that motor cortical activity also correlates with kinetic signals, including active hand force and joint torque. Here, we attempted to reconstruct torque trajectories of the shoulder and elbow joints from the activity of simultaneously recorded units in primary motor cortex (MI) as monkeys (Macaca Mulatta) made reaching movements in the horizontal plane. Using a linear filter decoding approach that considers the history of neuronal activity up to one second in the past, we found torque reconstruction performance nearly equal to that of Cartesian hand position and velocity, despite the considerably greater bandwidth of the torque signals. Moreover, the addition of delayed position and velocity feedback to the torque decoder substantially improved the torque reconstructions, suggesting that simple limb-state feedback may be useful to optimize BMI performance. These results may be relevant for BMI applications that require controlling devices with inherent, physical dynamics or applying forces to the environment.