Real-Time Control of the Hand by Intracortically Controlled Functional Neuromuscular Stimulation (original) (raw)
Related papers
PLoS ONE, 2009
Loss of hand use is considered by many spinal cord injury survivors to be the most devastating consequence of their injury. Functional electrical stimulation (FES) of forearm and hand muscles has been used to provide basic, voluntary hand grasp to hundreds of human patients. Current approaches typically grade pre-programmed patterns of muscle activation using simple control signals, such as those derived from residual movement or muscle activity. However, the use of such fixed stimulation patterns limits hand function to the few tasks programmed into the controller. In contrast, we are developing a system that uses neural signals recorded from a multi-electrode array implanted in the motor cortex; this system has the potential to provide independent control of multiple muscles over a broad range of functional tasks. Two monkeys were able to use this cortically controlled FES system to control the contraction of four forearm muscles despite temporary limb paralysis. The amount of wrist force the monkeys were able to produce in a one-dimensional force tracking task was significantly increased. Furthermore, the monkeys were able to control the magnitude and time course of the force with sufficient accuracy to track visually displayed force targets at speeds reduced by only one-third to one-half of normal. Although these results were achieved by controlling only four muscles, there is no fundamental reason why the same methods could not be scaled up to control a larger number of muscles. We believe these results provide an important proof of concept that brain-controlled FES prostheses could ultimately be of great benefit to paralyzed patients with injuries in the mid-cervical spinal cord. Citation: Pohlmeyer EA, Oby ER, Perreault EJ, Solla SA, Kilgore KL, et al. (2009) Toward the Restoration of Hand Use to a Paralyzed Monkey: Brain-Controlled Functional Electrical Stimulation of Forearm Muscles. PLoS ONE 4(6): e5924.
Brain-Controlled Electrical Stimulation Restores Continuous Finger Function
ABSTRACTBrain-machine interfaces have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user’s own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand. In a one-dimensional, continuous, finger-related target acquisition task, the monkey improved his success rate to 83% (1.5s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5s median acquisition, equivalent to chance) when attempting to use his temporarily paralyzed hand. With two monkeys under general anesthesia, we found FES alone could control the mo...
Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human
Scientific reports, 2017
Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients' own paralyzed limbs. To date, human studies have demonstrated an "all-or-none" type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate ...
Neural signals for command control and feedback in functional neuromuscular stimulation: a review
Journal of rehabilitation research and development, 1996
In current functional neuromuscular stimulation systems (FNS), control and feedback signals are usually provided by external sensors and switches, which pose problems such as donning and calibration time, cosmesis, and mechanical vulnerability. Artificial sensors are difficult to build and are insufficiently biocompatible and reliable for implantation. With the advent of methods for electrical interfacing with nerves and muscles, natural sensors are being considered as an alternative source of feedback and command signals for FNS. Decision making methods for higher level control can perform equally well with natural or artificial sensors. Recording nerve cuff electrodes have been developed and tested in animals and demonstrated to be feasible in humans for control of dorsiflexion in foot-drop and grasp in quadriplegia. Electromyographic signals, being one thousand times larger than electroneurograms, are easier to measure but have not been able to provide reliable indicators (e.g., ...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018
Loss of the upper limb imposes a devastating interruption to everyday life. Full restoration of natural arm control requires the ability to simultaneously control multiple degrees of freedom of the prosthetic arm and maintain that control over an extended period of time. Current clinically available myoelectric prostheses do not provide simultaneous control or consistency for transradial amputees. To address this issue, we have implemented a standard Kalman filter for continuous hand control using intramuscular electromyography (EMG) from both regenerative peripheral nerve interfaces (RPNI) and an intact muscle within non-human primates. Seven RPNIs and one intact muscle were implanted with indwelling bipolar intramuscular electrodes in two rhesus macaques. Following recuperations, function-specific EMG signals were recorded and then fed through the Kalman filter during a hand-movement behavioral task to continuously predict the monkey's finger position. We were able to reconstr...
Journal of Rehabilitation Research and Development, 2004
This paper reports on the initial phase of feasibility testing of a control strategy that uses myoelectric signals (MES) from wrist flexor and extensor muscles to control a hand-grasp neuroprosthesis for C7 tetraplegia. The control strategy was customized to the MES patterns produced during wrist flexion, extension, and relaxation for five able-bodied subjects and two individuals with C7 spinal cord injury. We evaluated the reliability with which the subjects could deliberately activate target neuroprosthesis states and control the degree of opening and closing of a computer-simulated hand using the myoelectric control strategy. Every subject was able to activate at least 99% of the target states for at least 1 continuous second, enough time to prove the activation was deliberate and to achieve significant hand opening or closing. Additionally, every subject was able to control the opening and closing of the simulated hand with enough proficiency to match greater than 87% of the target hand positions for at least 2 continuous seconds. Most of the inadvertent disturbances in simulated hand position were of a magnitude less than 10% of full range of motion for every subject. Future studies will incorporate the control strategy into an electrical stimulation system that opens and closes the hand of an individual with C7 tetraplegia.
Development of an invasive brain-machine interface with a monkey model
Chinese Science Bulletin, 2012
Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information. brain-machine interface, primary motor cortex, center-out paradigm, neural decoding, support vector machine, general regression neural network