Nuyujukian Paul, Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V
Neurosurgery Department, the Electrical Engineering Department, the Bioengineering Department, and Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305 USA.
Electrical Engineering Department, Stanford University, Stanford, CA 94305 USA.
Proc IEEE Inst Electr Electron Eng. 2017 Jan;105(1):66-72. doi: 10.1109/JPROC.2016.2586967. Epub 2016 Sep 12.
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.
脑机接口(BCIs)记录大脑活动并将信息转化为有用的控制信号。它们可用于通过控制诸如电脑光标和机器人肢体等终端效应器,帮助瘫痪患者恢复功能。通信神经假体是用于控制计算机或移动设备上用户界面的脑机接口。在此,我们通过对两只植入电极阵列的恒河猴进行打字任务模拟,展示了一种通信假体。当每次被提示一个符号/字母时,这两只猴子使用两种已知性能最高的脑机接口解码器打出单词和句子。平均而言,猴子J和猴子L分别实现了每分钟10.0个和7.2个单词(wpm)的打字速度,它们使用基于驻留的符号选择的仅速度二维脑机接口解码器,从一篇报纸文章中复制文本。使用还具有用于按键选择的离散点击功能的脑机接口解码器时,打字速度提高到了每分钟12.0个和7.8个单词。这些代表了使用脑机接口所实现的已知最高通信速度。然后,我们量化了比特率与打字速度之间的关系,发现其近似线性:以wpm为单位的打字速度几乎是每秒比特数的比特率的三倍。我们还比较了所实现的比特率和信息传输率的指标,并讨论了它们在实际打字场景中的适用性。尽管本研究无法模拟单词和句子规划的认知负荷的影响,但此处的研究结果证明了脑机接口作为通信接口的可行性,并代表了给定脑机接口吞吐量下预期实现的打字速度的上限。