SUMMARY Background We use our arms to transport and orient the hand which is used to grasp and manipulate objects. Upper limb paralysis or amputation limits a person’s ability to interact with their environment to accomplish activities of daily living. Brain-machine interfaces (BMIs) may provide a solution to restoring much of this function. Methods Two 96-channel intracortical microelectrodes were implanted in the motor cortex of an individual with tetraplegia. Thirteen weeks of BMI training were conducted with the goal of controlling an anthropomorphic prosthetic limb with 7 degrees-of-freedom (3D translation, 3D orientation, 1D grasping). Clinical measures of upper-limb function were used to assess the participant’s ability to use the prosthetic limb. Findings The participant demonstrated the ability to move the device freely in the three-dimensional (3D) workspace on the second day of training. After 13 weeks, robust 7 degree-of-freedom movements were performed routinely. Over time, performance on target-based reaching tasks improved in terms of success rate, completion time, and path efficiency. The participant was also able to use the prosthetic limb to perform skillful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper-limb function. Interpretation This study demonstrates that a person with chronic tetraplegia can perform consistent, natural, and complex movements with an anthropomorphic robotic arm to regain clinically significant function. Funding Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute
Arm movement is well represented in populations of neurons recorded from the motor cortex. Cortical activity patterns have been used in the new field of brain-machine interfaces to show how cursors on computer displays can be moved in two- and three-dimensional space. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment ('embodiment') has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects' cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control, previous experiments have lacked physical interaction even in cases where a robotic arm or hand was included in the control loop, because the subjects did not use it to interact with physical objects-an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.
Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.brain-machine interface ͉ neural prosthetics ͉ perturbation ͉ population vector algorithm A wealth of evidence associates learning with changes in neural activity (for reviews, see refs. 1-4). For example, the tuning functions of neurons in the motor cortex can change when monkeys adapt to perturbations that interfere with the execution (5-7) or visual feedback (8 -10) of their movements. Although the observed changes in neural activity in these studies are closely associated with the behavioral manifestation of learning, it is difficult to interpret the behavioral significance of the neural changes because the precise relationship between neural activity and behavioral output is unknown. However, recent developments in brain-computer interface technology now make it possible to control the activity of a cursor in a three-dimensional (3D) virtual environment using the spiking activity of ensembles of simultaneously recorded motor cortical units (11)(12)(13)(14)(15)(16). This ''braincontrol'' paradigm is unique in that the behavior, cursor movement, is solely the result of neural activity in the population under study. Thus, any mismatches between desired cursor motion and decoded cursor motion can only be corrected by altering the activity of these recorded neurons.The brain-computer interface also allows for a unique kind of perturbation that targets selective subsets of neurons. By altering the way that the firing activity of a subset of neurons is decoded and mapped into cursor movement, it is possible to test whether the tuning functions of the perturbed subset selectively change to compensate for the global error signal, or whether the entire population changes together.To study the response to selective perturbations of this neural network, monkeys were trained to perform center-out movements to eight equally spaced targets in a 3D virtual environm...
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