A recent article in Nature showed some remarkable advances in brain-computer interfacing. From the abstract:
Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute). Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements.
So the researchers weren't sending words directly from the brain into a computer in a way that the computer can understand. Instead, they asked the participant to think about writing the letters out, and these it was these electrical impulses they were able to map. As Ars Technica explained:
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.
High-performance brain-to-text communication via handwriting [Francis R. Willett, Donald T. Avansino, Leigh R. Hochberg, Jaimie M. Henderson & Krishna V. Shenoy / Nature]
Neural implant lets paralyzed person type by imagining writing [John Timmer / Ars Technica]