As per why the same approach is not well suited for speech, one (of many) components is the data bandwidth you need for speech to happen at a reasonable rate. For many applications the ML involved in the brain computer interface is only able to extract a few bits per second. It works for applications where the output is coarse (e.g. up/down/left/right) or where large delays are acceptable, but it's not the best fit for speech.

That's surprising. I thought it would be a lot higher than 3.3 bits for 10 fingers (probably not worth considering in this context).

> If they can hear the computer's speech interpretation in real time, they may be able to adjust their thoughts to get the result they want.

I wonder if thought adjustments are intuitive or tricky.