Two separate studies show that brain-to-computer interfaces could help decode brain signals into speech faster and more accurately than current tech.
in two areas associated with speech. The BCI was designed to detect brain activity when the patient was trying to speak. Those signals were then fed into an algorithm that associated certain brain activity patterns with phonemes — the sounds that make up speech. To train the algorithm, the researchers had the patient attempt toIn the, researchers surgically placed a paper-thin sheet containing 253 electrodes onto the brain of a person with severe paralysis from a brainstem stroke.
Although a 23 to 25 percent error rate isn’t good enough for everyday use, it’s a significant improvement over existing tech. In a press briefing, Edward Chang, chair of neurological surgery at UCSF and co-author of the UCSF study, noted that the effective rate of communication for existing technology is “laborious” at five to 15wpm when compared to the 150–250wpm for natural speech.
The UCSF study involved a BCI translating brain signals into facial expressions and modulated speech on a digital avatar.That said, these studies are more proof of concept than a technology that’s ready for prime time. One potential issue is that these treatments require long sessions to train the algorithm. However, researchers from both teams told press at a briefing that they were hopeful that algorithm training would be less intensive in the future.
Another issue is that the tech has to be easy enough for people to use at home, without requiring caregivers to go through complicated training. Brain implants are also invasive, and in these particular studies, the BCI had to be connected via wires to a device on the outside of the
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