Altogether, there were roughly 200 electrodes in the participant’s premotor cortex. Not all of them were informative for letter-writing. But for those that were, the authors performed a principal component analysis, which identified the features of the neural recordings that differed the most when various letters were imagined. Converting these recordings into a two-dimensional plot, it was obvious that the activity seen when writing a single character always clustered together. And physically similar characters — p and b, for example, or h, n, and r — formed clusters near each other. (The researchers also asked the participant to do punctuation marks like a comma and question mark and used a > to indicate a space and a tilde for a period.) Overall, the researchers found they could decipher the appropriate character with an accuracy of a bit over 94 percent, but the system required a relatively slow analysis after the neural data was recorded. To get things working in real time, the researchers trained a recurrent neural network to estimate the probability of a signal corresponding to each letter.
Despite working with a relatively small amount of data (only 242 sentences’ worth of characters), the system worked remarkably well. The lag between the thought and a character appearing on screen was only about half a second, and the participant was able to produce about 90 characters per minute, easily topping the previous record for implant-driven typing, which was about 25 characters per minute. The raw error rate was only about 5 percent, and applying a system like a typing autocorrect could drop the error rate down to only 1 percent. The tests were all done with prepared sentences. Once the system was validated, however, the researchers asked the participant to type out free-form answers to questions. Here, the speed went down a bit (to 75 characters a minute) and errors went up to 2 percent after autocorrection, but the system still worked. The findings have been published in the journal Nature.
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