Instant neural control of a movement signal

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Abstract

The activity of motor cortex (MI) neurons 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 (14 degrees x 14 degrees 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.

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Most cited references 5

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Real-time prediction of hand trajectory by ensembles of cortical neurons in primates.

J Wessberg, C Stambaugh, J Kralik … (2000)

Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

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Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex.

John K. Chapin, Karen Moxon, Ronald S. Markowitz … (1999)

To determine whether simultaneously recorded motor cortex neurons can be used for real-time device control, rats were trained to position a robot arm to obtain water by pressing a lever. Mathematical transformations, including neural networks, converted multineuron signals into 'neuronal population functions' that accurately predicted lever trajectory. Next, these functions were electronically converted into real-time signals for robot arm control. After switching to this 'neurorobotic' mode, 4 of 6 animals (those with > 25 task-related neurons) routinely used these brain-derived signals to position the robot arm and obtain water. With continued training in neurorobotic mode, the animals' lever movement diminished or stopped. These results suggest a possible means for movement restoration in paralysis patients.

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The Utah intracortical Electrode Array: a recording structure for potential brain-computer interfaces.

Edwin M. Maynard, Craig Nordhausen, Richard A. Normann (1997)

We investigated the potential of the Utah Intracortical Electrode Array (UIEA) to provide signals for a brain-computer interface (BCI). The UIEA records from small populations of neurons which have an average signal-to-noise ratio (SNR) of 6:1. We provide specific examples that show the activities of these populations of neurons contain sufficient information to perform control tasks. Results from a simple stimulus detection task using these signals as inputs confirm that the number of neurons present in a recording is significant in determining task performance. Increasing the number of units in a recording decreases the sensitivity of the response to the stimulus; decreasing the number of units in the recording, however, increases the variability of the response to the stimulus. We conclude that recordings from small populations of neurons, not single units, provide a reliable source of sufficiently stimulus selective signals which should be suitable for a BCI. In addition, the potential for simultaneous and proportional control of a large number of external devices may be realized through the ability of an array of microelectrodes such as the UIEA to record both spatial and temporal patterns of neuronal activation.