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一种高性能脑机接口。

A high-performance brain-computer interface.

作者信息

Santhanam Gopal, Ryu Stephen I, Yu Byron M, Afshar Afsheen, Shenoy Krishna V

机构信息

Department of Electrical Engineering, Stanford University, 330 Serra Mall, 319 Paul G. Allen Center for Integrated Systems Annex, Stanford, California 94305-4075, USA.

出版信息

Nature. 2006 Jul 13;442(7099):195-8. doi: 10.1038/nature04968.

DOI:10.1038/nature04968
PMID:16838020
Abstract

Recent studies have demonstrated that monkeys and humans 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 or electroencephalogram recordings using less- or non-invasive 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. 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 approximately 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.

摘要

最近的研究表明,猴子和人类能够利用大脑信号来引导计算机光标。脑机接口(BCI)也许有一天能帮助患有神经损伤或疾病的患者,但相对较低的系统性能仍然是一个主要障碍。事实上,使用BCI选择按键的速度和准确性仍远低于依赖眼球运动的系统。无论是BCI使用侵入性电极技术记录单个神经元群体的信号,还是使用较少侵入性或非侵入性技术记录脑电图,都是如此。在此,我们展示了一种使用植入猴子背侧运动前皮层的电极阵列设计并演示的性能比以前报道的高出许多倍的BCI。这些结果表明,一个能够在一系列键盘大小下运行的快速准确的按键选择系统是可行的(使用96个电极时,每秒可达6.5比特,或每分钟约15个单词)。即使记录质量随时间下降,通过前所未有的简短神经记录也能实现最高的信息吞吐量。这些性能结果及其对系统设计的影响应能大幅提高BCI在人类中的临床可行性。

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