Instant neural control of a movement signal

rons conveys movement intent sufficiently well to be used as a control signal to operate artificial devices, but until now this has called for extensive training or has been confined to a limited movement repertoire. Here we show how activity from a few (7–30) MI neurons can be decoded into a signal that a monkey is able to use immediately to move a computer cursor to any new position in its workspace (1472147 visual angle). Our results, which are based on recordings made by an electrode array that is suitable for human use, indicate that neurally based control of movement may eventually be feasible in paralysed humans. We recorded the activity of MI neurons in three Macaca mulatta monkeys that had been implanted with a Utah intracortical multi-electrode array. Monkeys used one hand to move a manipulandum that controlled the position of a cursor displayed on a video monitor. Using this positionfeedback signal, monkeys tracked a continuously moving visual target that began at an arbitrary location and followed a pseudo-random trajectory. We then used a linear filter method to test whether we could reliably reconstruct the hand trajectory from neural activity obtained in subsequent trials. Our linear filter method can be considered as a weighted sum of neural firing (here grouped in 50-ms bins) that takes into account the previous 1 s of activity (see supplementary information). Reconstruction accurately reflects hand trajectory, accounting for over 60% of the variance that was present in actual hand movements (r ¤0.6). One of the monkeys then used neural control to move the feedback cursor to different targets (Fig. 1). For this closed-loop version of the experiment, reconstructions were computed continuously online in real time. An average of 1 min of continuous tracking and recording was used to construct preliminary linear filters. Once this model was constructed, the continuous task was switched to a new task that required the cursor to be moved to stationary targets (size, 0.67), which were displayed one at a time at random locations on the monitor. During this time, hand control of the cursor position was substituted with neural control. For a period averaging 2 min, we constructed new decoding filters to relate firing to target position. Task performance continued under two conditions: the feedback cursor could be controlled either by hand motion or by the neural signal in pseudo-randomly interleaved trials. The monkey immediately used the neural-activity-based signal to carry out the task without any further training (see movie in supplementary information). During this time, the monkey intermittently made arm movements, including, but not restricted to, targetdirected hand motions (Fig. 1a). During some trials, however, the manipulandum was not moved; other muscle contractions may have occurred at this time, but these were not monitored (Fig. 1b). Targets were considered to be acquired only if they were reached within 20 s of their appearance. We found that cursor control was nearly as good as direct handpositional control. The time required to acquire targets using the neural signal was only slightly greater than that required for hand motions (Fig. 1c, d); this difference was not significant at a40.05 by a twosided Kolgorov–Smirnov test. By contrast, target acquisition was unsuccessful (¤1 min) if filters were randomly shuffled between cells or if a random number was added to the filter coefficients, indicating that properly constructed filters are necessary for neural control to operate. The marked, immediate success of the linear-regression method compared with other approaches may be related to the lack of strong assumptions about neuronal firing, to the power of the linear-regression method, or to the number and type of cells used for decoding. Neuroprosthetic devices will comprise two learning systems: a mathematical algorithm and the subject’s brain. A primary role of the algorithm is to transform neural activity into a control signal that operates in a functionally usable region of space. The utility of filters constructed during neural control suggests that visual and brief communications