Dexterous Control Research Articles

Reinforcement Learning Aided Robot-Assisted Navigation: A Utility and RRT Two-Stage Approach

This work proposes a robot-assisted navigation approach based on user intent adjustment, in the context of robotic walkers. Walkers are prescribed to users with gait disorders so that they can support their body weight on the upper limbs, however, the manipulation of such devices can be cumbersome for some users. Common problems for the users are lack of dexterous upper limb control and visual impairments. These problems can render walkers’ users helpless, making them unable to operate these devices effectively and efficiently. We present a new approach to robot-assisted navigation using a utility decision and safety analysis procedure with user intent adjustments learned by reinforcement learning (RL) and supported on a rapidly-exploring random tree inspired algorithm. The proposed approach offers full control of the assistive platform to the user until obstacles are detected. In dangerous scenarios, corrections are computed in order that the assistive platform avoids collisions and follows social norms, effectively guiding the user through the environment while enforcing safer routes. The experimental validation was carried out in a virtual environment and in a real world scenario using a robotic walker built in our lab (ISR-AIWALKER). Experimental results have shown that the proposed approach provides a reliable solution to the robot-assisted navigation of a robotic walker, in particular the use of utility theory to evaluate candidate motions together with a RL model increases the safety of the user’s navigation.

Better grip force control by attending to the controlled object: Evidence for direct force estimation from visual motion

Estimating forces acting between our hand and objects is essential for dexterous motor control. An earlier study suggested that vision contributes to the estimation by demonstrating changes in grip force pattern caused by delayed visual feedback. However, two possible vision-based force estimation processes, one based on hand position and another based on object motion, were both able to explain the effect. Here, to test each process, we examined how visual feedback of hand and object each contribute to grip force control during moving an object (mass) connected to the grip by a damped-spring. Although force applied to the hand could be estimated from its displacement, we did not find any improvements by the hand feedback. In contrast, we found that visual feedback of object motion significantly improved the synchrony between grip and load forces. Furthermore, when both feedback sources were provided, the improvement was observed only when participants were instructed to direct their attention to the object. Our results suggest that visual feedback of object motion contributes to estimation of dynamic forces involved in our actions by means of inverse dynamics computation, i.e., the estimation of force from motion, and that visual attention directed towards the object facilitates this effect.

Relationship between Interhemispheric Inhibition and Dexterous Hand Performance in Musicians and Non-musicians

Interhemispheric inhibition (IHI) is essential for dexterous motor control. Small previous studies have shown differences in IHI in musicians compared to non-musicians, but it is not clear whether these differences are robustly linked to musical performance. In the largest study to date, we examined IHI and comprehensive measures of dexterous bimanual performance in 72 individuals (36 musicians and 36 non-musicians). Dexterous bimanual performance was quantified by speed, accuracy, and evenness derived from a series of hand tasks. As expected, musicians significantly outperformed non-musicians. Surprisingly, these performance differences could not be simply explained by IHI, as IHI did not significantly differ between musicians and non-musicians. However, canonical correlation analysis revealed a significant relationship between combinations of IHI and performance variables in the musician group. Specifically, we identified that IHI may contribute to the maintenance of evenness regardless of speed, a feature of musical performance that may be driven by practice with a metronome. Therefore, while IHI changes by themselves may not be sufficient to explain superior hand dexterity exhibited by musicians, IHI may be a potential neural correlate for specific features of musical performance.

Classifying hand sensorimotor functions of the chronic kidney disease patients using novel manual tactile test and pinch-holding-up activity.

Hand function deterioration brings about inconvenience to the daily lives of the chronic kidney disease patients. However, a full spectrum of hand function examination is absent. Therefore, this study aimed to classify the hand sensorimotor functions of the chronic kidney disease patients using the novel sensorimotor assessment tools, manual tactile test (MTT) and pinch-holding-up activity (PHUA) test, and explore the feasibility in comparison with traditional evaluations in the clinical practice. 68 stage-5 chronic kidney disease patients and 50 healthy subjects were recruited in this study. A series of conventional evaluations and two novel hand function tools, manual tactile test and pinch-holding-up activity test were conducted from the perspective of hand dexterity, sensory input threshold, force generation and sensorimotor control. Independent t-test was used to find out group differences and the receiver operating characteristic curve was used to determine accuracy of the tests. In our results, significant reduction of hand dexterity, sensory input, force generation and sensorimotor control was found in patients from an overall perspective. This trend was discovered to be the same when dividing the subjects into the old and young age group. From the receiver operator characteristic curves, nearly all the areas under the curve of all tests were over 0.8. The novel evaluation tools, the manual tactile test and pinch-holding-up activity, were found to have comparable or even better accuracy than the traditional ones. The shape and weight subtests of the manual tactile test displayed the highest accuracy. To sum up, by incorporating the novel and conventional assessment tests, this study built up the fundamental understanding of the hand functions in multiple dimensions and consolidate the clinical merits of applying the two novel tools, manual tactile test and pinch-holding-up activity, on chronic kidney disease patients.

Visualized Evidences for Detecting Novelty in Myoelectric Pattern Recognition using 3D Convolutional Neural Networks.

Although myoelectric pattern recognition (MPR) has been considered as a milestone technique to enable dexterous control of multiple degrees of freedom, outlier data interference (i.e., novelty) is a big issue affecting stability of conventional MPR control and its wide applications. Inspired by video classification techniques, we propose a novel method using 3-dimensional (3D) convolutional neural network (CNN) for extracting sufficient spatial-temporal features from high-density surface electromyogram (EMG) recordings processed as a video stream where time-varying information of muscular activity was taken into account. Given the targeted task patterns accurately characterized by the proposed method, it is straightforward to discriminate and then reject outlier data interferences regarded as untargeted and unlearnt patterns. Meanwhile, the strength of the 3D CNN in discriminating targeted task patterns through spatial-temporal features is further visualized and confirmed by t-Distributed Stochastic Neighbor Embedding (t-SNE). The performance of the proposed method was evaluated with surface EMG recorded by two 6 × 8 electrode arrays placed over forearm flexors and extensors of 3 subjects performing 7 targeted and 4 outlier motions. The visualized results by t-SNE showed the targeted task patterns were separated with each concentrated into a small region, while the outlier patterns were scattered around. On this basis, a traditional Mahalanobis Distance (MD) based method was applied for novelty detection and targeted task classification. Finally, the proposed method yielded averaged error rate of 10.98% for the targeted task patterns and <; 5% for all outlier data, respectively, which outperformed a common baseline method. All these findings demonstrate the advantage of characterizing myoelectric patterns using the proposed method and the potential of applying it to reject outlier data interference.

Contribution of the Cerebellum to Predictive Motor Control and Its Evaluation in Ataxic Patients.

Goal-directed movements are predictive and multimodal in nature, especially for moving targets. For instance, during a reaching movement for a moving target, humans need to predict both motion of the target and movement of the limb. Recent computational studies show that the cerebellum predicts current and future states of the body and its environment using internal forward models. Sensory feedback signals from the periphery have delays in reaching the central nervous system, ranging between tens to hundreds of milliseconds. It is well known in engineering that feedback control based on time-delayed inputs can result in oscillatory and often unstable movements. In contrast, the brain predicts a current state from a previous state using forward models. This predictive mechanism most likely underpins stable and dexterous control of reaching movements. Although the cerebro-cerebellum has long been suggested as loci of various forward models, few methods are available to evaluate accuracy of the forward models in patients with cerebellar ataxia. Recently, we developed a non-invasive method to analyze receipt of motor commands in terms of movement kinematics for the wrist joint (Br/Kr ratio). In the present study, we have identified two components (F1 and F2) of the smooth pursuit movement. We found that the two components were in different control modes with different Br/Kr ratios. The major F1 component in a lower frequency range encodes both velocity and position of the moving target (higher Br/Kr ratio) to synchronize movement of the wrist joint with motion of the target in a predictive manner. The minor F2 component in a higher frequency range is biased to position control in order to generate intermittent small step-wise movements. In cerebellar patients, the F1 component shows a selective decrease in the Br/Kr ratio, which is correlated with decrease in accuracy of the pursuit movement. We conclude that the Br/Kr ratio of the F1 component provides a unique parameter to evaluate accuracy of the predictive control. We also discuss the pathophysiological and clinical implications for clinical ataxiology.

Robotic interfaces for cognitive psychology and embodiment research: A research roadmap.

Advanced human-machine interfaces render robotic devices applicable to study and enhance human cognition. This turns robots into formidable neuroscientific tools to study processes such as the adaptation between a human operator and the operated robotic device and how this adaptation modulates human embodiment and embodied cognition. We analyze bidirectional human-machine interface (bHMI) technologies for transparent information transfer between a human and a robot via efferent and afferent channels. Even if such interfaces have a tremendous positive impact on feedback loops and embodiment, advanced bHMIs face immense technological challenges. We critically discuss existing technical approaches, mainly focusing on haptics, and suggest extensions thereof, which include other aspects of touch. Moreover, we point out other potential constraints such as limited functionality, semi-autonomy, intent-detection, and feedback methods. From this, we develop a research roadmap to guide understanding and development of bidirectional human-machine interfaces that enable robotic experiments to empirically study the human mind and embodiment. We conclude the integration of dexterous control and multisensory feedback to be a promising roadmap towards future robotic interfaces, especially regarding applications in the cognitive sciences. This article is categorized under: Computer Science > Robotics Psychology > Motor Skill and Performance Neuroscience > Plasticity.

Virtual Integration Environment as an Advanced Prosthetic Limb Training Platform.

Background: Despite advances in prosthetic development and neurorehabilitation, individuals with upper extremity (UE) loss continue to face functional and psychosocial challenges following amputation. Recent advanced myoelectric prostheses offer intuitive control over multiple, simultaneous degrees of motion and promise sensory feedback integration, but require complex training to effectively manipulate. We explored whether a virtual reality simulator could be used to teach dexterous prosthetic control paradigms to individuals with UE loss.Methods: Thirteen active-duty military personnel with UE loss (14 limbs) completed twenty, 30-min passive motor training sessions over 1–2 months. Participants were asked to follow the motions of a virtual avatar using residual and phantom limbs, and electrical activity from the residual limb was recorded using surface electromyography. Eight participants (nine limbs), also completed twenty, 30-min active motor training sessions. Participants controlled a virtual avatar through three motion sets of increasing complexity (Basic, Advanced, and Digit) and were scored on how accurately they performed requested motions. Score trajectory was assessed as a function of time using longitudinal mixed effects linear regression.Results: Mean classification accuracy for passive motor training was 43.8 ± 10.7% (14 limbs, 277 passive sessions). In active motor sessions, >95% classification accuracy (which we used as the threshold for prosthetic acceptance) was achieved by all participants for Basic sets and by 50% of participants in Advanced and Digit sets. Significant improvement in active motor scores over time was observed in Basic and Advanced sets (per additional session: β-coefficient 0.125, p = 0.022; β-coefficient 0.45, p = 0.001, respectively), and trended toward significance for Digit sets (β-coefficient 0.594, p = 0.077).Conclusions: These results offer robust evidence that a virtual reality training platform can be used to quickly and efficiently train individuals with UE loss to operate advanced prosthetic control paradigms. Participants can be trained to generate muscle contraction patterns in residual limbs that are interpreted with high accuracy by computer software as distinct active motion commands. These results support the potential viability of advanced myoelectric prostheses relying on pattern recognition feedback or similar controls systems.

Dexterous manipulation and control with volumetric muscles

We propose a framework for simulation and control of the human musculoskeletal system, capable of reproducing realistic animations of dexterous activities with high-level coordination. We present the first controllable system in this class that incorporates volumetric muscle actuators, tightly coupled with the motion controller, in enhancement of line-segment approximations that prior art is overwhelmingly restricted to. The theoretical framework put forth by our methodology computes all the necessary Jacobians for control, even with the drastically increased dimensionality of the state descriptors associated with three-dimensional, volumetric muscles. The direct coupling of volumetric actuators in the controller allows us to model muscular deficiencies that manifest in shape and geometry, in ways that cannot be captured with line-segment approximations. Our controller is coupled with a trajectory optimization framework, and its efficacy is demonstrated in complex motion tasks such as juggling, and weightlifting sequences with variable anatomic parameters and interaction constraints.

Characterizing the balance-dexterity task as a concurrent bipedal task to investigate trunk control during dynamic balance

The purpose of the study was to characterize the Balance-Dexterity Task as a means to investigate a concurrent bipedal lower-extremity task and trunk control during dynamic balance. The task combines aspects of single-limb balance and the lower-extremity dexterity test by asking participants to stand on one limb while compressing an unstable spring with the contralateral limb to an individualized target force. Nineteen non-disabled participants completed the study, and performance measures for the demands of each limb – balance and dexterous force control – as well as kinematic and electromyographic measures of trunk control were collected. Given five practice trials, participants achieved compression forces ranging from 100 to 139 N (mean 121.2 ± 12.3 N), representing 14.4–23.0% of body weight (mean 18.7 ± 2.4%), which were then presented as target forces during test trials. Dexterous force control coefficient of variation and average magnitude of the center of pressure (COP) resultant velocity were associated such that greater variability in force control was accompanied by greater COP velocity (R = 0.598, p = 0.007). Trunk coupling, quantified as the coefficient of determination (R2) of a frontal plane thorax and pelvis angle-angle plot, varied independently of any measure of balance or dexterous force control. The Balance-Dexterity Task is a continuous, dynamic balance task where bipedal coordination and trunk coupling can be concurrently observed and studied.

A Novel Time-Domain Descriptor for Improved Prediction of Upper Limb Movement Intent in EMG-PR System.

Electromyogram pattern recognition (EMG-PR) based control is a potential method capable of providing intuitively dexterous control functions in upper limb prostheses. Meanwhile, the feature extraction method adopted in EMG-PR based control is considered as an important factor that influences the performance of the prostheses. By exploiting the limitations of the existing feature extraction methods, this study proposed a new feature extraction method to effectively characterize EMG signal patterns associated with different limb movement intent. The performance of the proposed 2-dimensional novel time-domain feature set (NTDFS) was investigated using classification accuracy and feature space separability metrics across five subjects' EMG recordings, and compared with four different existing methods. In comparison to four other previously proposed feature extraction methods, the NTDFS achieved significantly better performance with increment in accuracy in the range of 5.20% ∼ 8.40% at p<0.05. Additionally, by applying principal component analysis (PCA) technique, the PCA feature space for NTDFS show obvious class separability in comparison to the other existing feature extraction methods. Thus, the proposed NTDFS may facilitate the development of accurate and robust clinically viable EMG-PR based prostheses.

Flexibility of Finger Activation Patterns Elicited through Non-invasive Multi-Electrode Nerve Stimulation.

The inability to effectively activate and control skeletal muscles is a common impairment following a variety of neurological conditions or injuries. One common approach to restoring or augmenting this impairment is the use of external electrical stimulation of the muscles, called functional electrical stimulation (FES). Typically targeted directly at the anatomical muscle belly, existing methodologies often involve high current amplitudes, limited superficial muscle activation, and early onset of muscle fatigue. We have recently explored the capabilities of a non-invasive peripheral nerve stimulation method for the dexterous control of finger and hand muscles. Further development of our stimulation system has enabled us to manually search across a variety of stimulation locations with increased consistency and efficiency. This study examined the preliminary results in two subjects of an automated stimulation system which can rapidly characterize a large combination of stimulation electrodes. Our preliminary findings suggested that the stimulation grid was able to produce a number of clustered EMG activities and finger forces. This robust ability to flexibly generate different grasp patterns demonstrates the promise of the methodology in future applications for FES and rehabilitation.

Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals.

This paper focuses on identification of an effective pattern recognition scheme with the least number of time domain features for dexterous control of prosthetic hand to recognize the various finger movements from surface electromyogram (EMG) signals. Eight channels EMG from 8 able-bodied subjects for 15 individuals and combined finger activities have been considered in this work. In this work, an attempt has been made to recognize a number of classes with the least number of features. Therefore, EMG signals are pre-processed using dual tree complex wavelet transform to improve the discriminating capability of features and time domain features such as zero crossing, slope sign change, mean absolute value, and waveform length is extracted from the pre-processed data. The performance of extracted features is studied with different classifiers such as linear discriminant analysis, naive Bayes classifier, quadratic support vector machine and cubic support vector machine with and without feature selection algorithms. The feature selection has been studied using particle swarm optimization (PSO) and ant colony optimization (ACO) with different number of features to identify the effect of features. The results demonstrated that naive Bayes classifier with ant colony optimization shows an average classification accuracy of 88.89% with a response time of 0.058025ms for recognizing the 15 different finger movements with 16 features with significant difference in accuracy compared to SVM classifier with feature selection for a significance level of 0.05. There is no significant difference in the accuracy, specificity and sensitivity of an SVM classifier with and without feature selection. But the processing time is significantly more than the LDA and NB classifier. The PSO and ACO results revealed that slope sign changes contribute to recognizing the activity. In PSO, mean absolute value has been found to be effective compared to waveform length, contradictory with ACO. Further, the zero crossings have been found to be not effective in classification of finger movements in both the methods.

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.

Online mapping of EMG signals into kinematics by autoencoding

BackgroundIn this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach.MethodsSeven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics.ResultsBoth methods allowed a completion rate close to 100%, however AEN outperformed SOA for all other performance metrics, e.g. it allowed to perform the tasks on average in half the time with respect to SOA. Moreover, the amount of information transferred by the proposed method in bit/s was nearly twice the throughput of SOA.ConclusionsThese results show that autoencoders can map EMG signals into kinematics with the potential of providing intuitive and dexterous control of artificial limbs for amputees.

Influences of 12-Week Physical Activity Interventions on TMS Measures of Cortical Network Inhibition and Upper Extremity Motor Performance in Older Adults-A Feasibility Study.

Objective: Data from previous cross-sectional studies have shown that an increased level of physical fitness is associated with improved motor dexterity across the lifespan. In addition, physical fitness is positively associated with increased laterality of cortical function during unimanual tasks; indicating that sedentary aging is associated with a loss of interhemispheric inhibition affecting motor performance. The present study employed exercise interventions in previously sedentary older adults to compare motor dexterity and measure of interhemispheric inhibition using transcranial magnetic stimulation (TMS) after the interventions.Methods: Twenty-one community-dwelling, reportedly sedentary older adults were recruited, randomized and enrolled to a 12-week aerobic exercise group or a 12-week non-aerobic exercise balance condition. The aerobic condition was comprised of an interval-based cycling “spin” activity, while the non-aerobic “balance” exercise condition involved balance and stretching activities. Participants completed upper extremity dexterity batteries and estimates of VO2max in addition to undergoing single (ipsilateral silent period—iSP) and paired-pulse interhemispheric inhibition (ppIHI) in separate assessment sessions before and after study interventions. After each intervention during which heart rate was continuously recorded to measure exertion level (load), participants crossed over into the alternate arm of the study for an additional 12-week intervention period in an AB/BA design with no washout period.Results: After the interventions, regardless of intervention order, participants in the aerobic spin condition showed higher estimated VO2max levels after the 12-week intervention as compared to estimated VO2max in the non-aerobic balance intervention. After controlling for carryover effects due to the study design, participants in the spin condition showed longer iSP duration than the balance condition. Heart rate load was more strongly correlated with silent period duration after the Spin condition than estimated VO2.Conclusions: Aging-related changes in cortical inhibition may be influenced by 12-week physical activity interventions when assessed with the iSP. Although inhibitory signaling is mediates both ppIHI and iSP measures each TMS modality likely employs distinct inhibitory networks, potentially differentially affected by aging. Changes in inhibitory function after physical activity interventions may be associated with improved dexterity and motor control at least as evidence from this feasibility study show.

Implications of Robotic Surgery

The interest that I have in robotic surgery stems from the discrepancies between literature at the time that my current hospital purchased their system and the aggressive marketing campaign from the parent company. In urology, the robot is marketed as “nerve-sparing” during prostatectomies and in cardiac it allowed more dexterity or finer motor control. The bulk of the concerns center around issues such as system resetting; electrosurgical arch; increased surgical time; prolonged carbon dioxide insufflation; unnecessarily extended amounts of time under anesthesia; and extremely high costs to purchase and maintain the system on site. A cost-based analysis and a surgical time consideration of robotic versus laparoscopic and open procedures disprove the regularly cited benefits of using a robotic console over the trained surgeon hands for not only surgical risk but financial benefits to both the facility and patient. I gathered records of robotic, laparoscopic and open procedures, the time each takes, the financials for instrumentation and the legal issues or complication associated for each. The robotic technique is a greater cost than any procedure comparable and at an increase of surgical and/or anesthetic time. The greater the anesthetic time, the more potential harm to patients. The greater the surgical time, the higher the risk from increased pneumoperitoneum CO2 insufflation on the cardiovascular and respiratory systems. The higher the cost ratio, the less benefit for the practicing facility. Legalities are a new issue to consider when the robot or the parent company is found at fault considering the health insurance agencies have taken a stand on compensation for only the diagnosis and not the techniques or technology utilized for that treatment.

Combination of EMG Features and Stability Index for Finger Movements Recognition

Even though extensive research have been done on electromyogram (EMG) based prosthetic hand control, relatively lesser attention have been received for control of individual prosthetic hand fingers. An EMG based recognition of individual finger movements will enable a more dexterous control of prosthetic hands. This paper reports the recognition of index finger movements: flexion-extension and abduction-adduction based on two channel EMG. The focus was on deriving an EMG feature vector through combination of features for higher recognition rate based on a stability index. Feature vector derived through linear discriminant analysis of the average combination of three time and frequency domain features with the highest stability index resulted into a recognition of 98%. The recognition was using Levenberg-Marquardt (L-M) algorithm based back propagation neural network. The experimental result shows that the stability index holds promise in deriving a feature vector for higher recognition rate of finger movements.

Neural control of finger movement via intracortical brain–machine interface

Objective. Intracortical brain–machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ = 0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s−1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step towards full and dexterous control of neural prosthetic devices.

Origins of Common Neural Inputs to Different Compartments of the Extensor Digitorum Communis Muscle

The extensor digitorum communis (EDC) is a multi-compartment muscle that allows dexterous extension of the four digits. However, the level of common input shared across different compartments of this muscle is not well understood. We seek to systematically characterize the common and independent neural input, originated from different levels of the central nervous system, to the different compartments. A motor unit (MU) coherence analysis was used to capture the different sources of common and independent input, by quantifying the coherence of MU discharge between different compartments. The MU activities were obtained from decomposition of surface electromyogram recordings. Our results showed that the MU coherence across different muscle compartments accounted for only a small proportion (<20%) of the total input in the alpha (5–12 Hz) and beta (15–30 Hz) bands, but was a major driver (>60%) in the delta (1–4 Hz) band. Additionally, cross-compartment coherence between the middle and ring-little fingers tended to be higher as compared with other finger combinations. Overall, the common input shared across different fingers are found to be at low to moderate levels, in comparison with the total input, which allows dexterous control of individual digits with some degree of coordinated control of multiple digits.