Arm movement is well represented in populations of neurons recorded from the motor cortex [1] [2] [3] [4] [5] [6] [7] . Cortical activity patterns have been used in the new field of brain-machine interfaces [8] [9] [10] [11] to show how cursors on computer displays can be moved in two-and threedimensional space [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] . 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 23 , previous experiments have lacked physical interaction even in cases where a robotic arm 16, 19, 24 or hand 20 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-offreedom 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. Two monkeys were implanted with intracortical microelectrode arrays in their primary motor cortices. Each monkey used the signals to control a robotic arm to feed itself. The robotic arms used in these experiments had five degrees of freedom: three at the shoulder, one at the elbow and one at the hand. Like a human arm, they permitted shoulder flexion/extension, shoulder abduction/adduction, internal/ external rotation of the shoulder and flexion/extension of the elbow. The hand consisted of a motorized gripper with the movement of its two 'fingers' linked, providing proportional control of the distance between them. Monkeys were first trained to operate the arm using a joystick (Supplementary Methods). Their own arms were then restrained and the prosthetic arm was controlled with populations of single-and multi-unit spiking activity from the motor cortex. The neural activity was differentially modulated when food was presented at different target locations in front of the monkey. Based on previous work 24 , we used this modulation to represent velocity of the prosthetic arm's endpoint (a point between the fingertips of the hand/gripper) as an expression of the intention to move 2, 3 . The recorded signal was also used by the subject to open and close the gripper as it grasped and moved the food to the mouth. The endpoint velocity and gripper command were extracted from the instantaneous firing rates of simultaneously recorded units using a real-time extraction algorithm. 7,25-27 or closed-loop experiments 12-24 , but here we show that a simple algorithm functioned well in this application. The population vector algorithm 28 (PVA) used here was similar to algorithms used in some cursor-control experiments 15, 21 . It relies on the directional tuning of each unit, characterized by a single preferred direction in which the unit fires maximally. The real-time population vector is essentially a vector sum of the preferred directions of the units in the recorded population, weighted by the instantaneous firing rates of the units, and was taken here to represent four dimensions-velocity of the endpoint in an arbitrary extrinsic threedimensional cartesian coordinate frame, and aperture velocity between gripper fingers (fourth dimension). The endpoint velocity was integrated to obtain endpoint position, and converted to a jointangular command position, for each of the robot's four degrees of freedom, using inverse kinematics. Degree-of-freedom redundancy was solved by constraining elbow elevation in a way that resulted in natural-looking movements (Supplementary Methods). As the monkey's cortical command signal was decoded in small time-increments (30 ms), the control was effectively continuous and the animal was able to continuously change the speed and direction of arm movement and gripper aperture. Details of the control algorithm are in Supplementary Methods. To demonstrate fully embodied control (Fig. 1 ), monkeys learned a continuous self-feeding task involving real-time physical interaction between the arm, a food target, a presentation device (designed to record the target's three-dimensional location) and their mouth. Unlike short control windows used in previous studies, each monkey controlled the arm and gripper continuously during an entire session (not only during reaching and retrieval movements but also during loading/unloading and between trials). The task was challenging owing to the positional accuracy required (about 5-10 mm from the target centre position at the time of gripper closing). The required accuracy for retrieval was much lower because the monkey could move its head to meet the gripper. Supplementary Video 1 shows monkey A performing seven consecutive successful trials of continuous self-feeding. It can be seen from the video that the monkey was still chewing on the previous piece of food while reaching for the next one. It can also be seen that the monkey was able to move its head and eyes naturally without affecting control of the prosthetic arm. Example signals from the last four trials of the video show the correspondence between the spike signals of the 116 units used for control during that session and the resulting arm and gripper movement (Fig. 2) . Monkey A performed 2 days of the continuous self-feeding task with a combined success rate of 61% (67 successes out of 101 attempted trials on the first day, and 115 out of 197 on the second day). To put this success rate in perspective, a task of comparable difficulty to a previous virtual cursor control study from our group 15 would be to simply move the prosthetic arm's endpoint near the target
In the field of neuroscience, a very interesting study published in Nature REF demonstrates how a monkey can learn to control a robot arm without using any encoder feedback.
Cortical control of a prosthetic arm for self-feeding
Nature
Nature
Medicine