Another piece slots in to the mind-machine interface puzzle: via George Dvorsky comes news that University of Utah neuroboffins have decoded individual words from embedded electrode scans of brain activity.
The University of Utah research team placed grids of tiny microelectrodes over speech centers in the brain of a volunteer with severe epileptic seizures. The man already had a craniotomy – temporary partial skull removal – so doctors could place larger, conventional electrodes to locate the source of his seizures and surgically stop them.
Using the experimental microelectrodes, the scientists recorded brain signals as the patient repeatedly read each of 10 words that might be useful to a paralyzed person: yes, no, hot, cold, hungry, thirsty, hello, goodbye, more and less.
Later, they tried figuring out which brain signals represented each of the 10 words. When they compared any two brain signals – such as those generated when the man said the words “yes” and “no” – they were able to distinguish brain signals for each word 76 percent to 90 percent of the time.
As always with this sort of story, though, it’s early days yet:
When they examined all 10 brain signal patterns at once, they were able to pick out the correct word any one signal represented only 28 percent to 48 percent of the time – better than chance (which would have been 10 percent) but not good enough for a device to translate a paralyzed person’s thoughts into words spoken by a computer.
“This is proof of concept,” Greger says, “We’ve proven these signals can tell you what the person is saying well above chance. But we need to be able to do more words with more accuracy before it is something a patient really might find useful.”
So you’ll have to wait a little longer for that comfy little skull-cap that’ll read your as-yet-unwritten novel straight out of your head (worse luck). But proof-of-concept’s better than nothing, especially for a technology that – even comparatively recently – was considered to be pure science fiction.