A Non-Human Primate Brain-Computer Typing Interface.
A Non-Human Primate Brain-Computer Typing Interface.
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DOI：
10.1109/jproc.2016.2586967
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发表时间：
2017-01
期刊：
影响因子：
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通讯作者：
Shenoy KV
中科院分区：
文献类型：
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作者：
Nuyujukian P;Kao JC;Ryu SI;Shenoy KV
脑机接口（BCI）记录大脑活动并将信息转化为有用的控制信号。它们可以通过控制计算机光标和机器人肢体等末端执行器来恢复瘫痪患者的功能。通信神经假体是控制计算机或移动的设备上的用户界面的BCI。在这里，我们展示了一个沟通假体，通过模拟打字任务与两个恒河猴植入电极阵列。这些猴子使用了两个已知性能最高的BCI解码器，在一次提示一个符号/字母时，打出单词和句子。平均而言，猴子J和L分别实现了每分钟10.0和7.2个单词（wpm）的打字速度，使用具有基于驻留的符号选择的仅速度二维BCI解码器从报纸文章中复制文本。使用BCI解码器，也具有离散点击键选择功能，打字速度增加到12.0和7.8 wpm。这些代表了使用BCI已知的最高通信速率。然后，我们量化了比特率和打字速率之间的关系，发现它近似线性：以每分钟文字为单位的打字速率几乎是以每秒比特为单位的比特率的三倍。我们还比较了实现比特率和信息传输速率的指标，并讨论了它们对现实世界打字场景的适用性。虽然这项研究不能模拟单词和句子规划的认知负荷的影响，这里的研究结果表明，BCI作为通信接口的可行性，并代表一个给定的BCI吞吐量的预期实现打字率的上限。
Brain-computer interfaces (BCIs) record brain activity and translate the information into useful control signals. They can be used to restore function to people with paralysis by controlling end effectors such as computer cursors and robotic limbs. Communication neural prostheses are BCIs that control user interfaces on computers or mobile devices. Here we demonstrate a communication prosthesis by simulating a typing task with two rhesus macaques implanted with electrode arrays. The monkeys used two of the highest known performing BCI decoders to type out words and sentences when prompted one symbol/letter at a time. On average, Monkeys J and L achieved typing rates of 10.0 and 7.2 words per minute (wpm), respectively, copying text from a newspaper article using a velocity-only two dimensional BCI decoder with dwell-based symbol selection. With a BCI decoder that also featured a discrete click for key selection, typing rates increased to 12.0 and 7.8 wpm. These represent the highest known achieved communication rates using a BCI. We then quantified the relationship between bitrate and typing rate and found it approximately linear: typing rate in wpm is nearly three times bitrate in bits per second. We also compared the metrics of achieved bitrate and information transfer rate and discuss their applicability to real-world typing scenarios. Although this study cannot model the impact of cognitive load of word and sentence planning, the findings here demonstrate the feasibility of BCIs to serve as communication interfaces and represent an upper bound on the expected achieved typing rate for a given BCI throughput.