1. Department of Electrical Engineering, Stanford University, 330 Serra Mall, 319 Paul G. Allen Center for Integrated Systems Annex, Stanford, California 94305-4075, USA
2. Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Drive, Edwards Building, R-297, Stanford, California 94305-5327, USA
3. Medical Scientist Training Program, Stanford University School of Medicine, 251 Campus Drive, MSOB 309, Stanford, California 94305-5404, USA
4. Neurosciences Program, Stanford University School of Medicine, Stanford, California 94305, USA
5. *These authors contributed equally to this work

Correspondence to: Krishna V. Shenoy^1,4 Correspondence and requests for materials should be addressed to K.V.S. (Email: shenoy@stanford.edu).

Abstract

Recent studies have demonstrated that monkeys^{1, 2, 3, 4} and humans^{5, 6, 7, 8, 9} can use signals from the brain to guide computer cursors. Brain–computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques^{1, 2, 3, 4, 5, 7, 8} or electroencephalogram recordings using less-⁶ or non-invasive⁹ techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported^{9, 10}. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.

1. Department of Electrical Engineering, Stanford University, 330 Serra Mall, 319 Paul G. Allen Center for Integrated Systems Annex, Stanford, California 94305-4075, USA
2. Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Drive, Edwards Building, R-297, Stanford, California 94305-5327, USA
3. Medical Scientist Training Program, Stanford University School of Medicine, 251 Campus Drive, MSOB 309, Stanford, California 94305-5404, USA
4. Neurosciences Program, Stanford University School of Medicine, Stanford, California 94305, USA
5. *These authors contributed equally to this work

Correspondence to: Krishna V. Shenoy^1,4 Correspondence and requests for materials should be addressed to K.V.S. (Email: shenoy@stanford.edu).

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