It does not take an infinite number of monkeys to type a passage of Shakespeare. Instead, it takes a single monkey equipped with brain-sensing technology – and a cheat sheet.
That technology, developed byStanford Bio-X scientists Krishna Shenoy, a professor of electrical engineering at Stanford, and postdoctoral fellow Paul Nuyujukian, directly reads brain signals to drive a cursor moving over a keyboard. In an experiment conducted with monkeys, the animals were able to transcribe passages from the New York Timesand Hamlet at a rate of up to 12 words per minute.
Ref: A Nonhuman Primate Brain–Computer Typing Interface. Proceedings of IEEE (12 August 2016) | DOI: 10.1109/JPROC.2016.2586967
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 2-D 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 as communication interfaces and represent an upper bound on the expected achieved typing rate for a given BCI throughput.