The new brain-computer interface (BCI) technology can translate a person's inner thoughts into words, activated only when they 'think' a specific mental password.
Stanford University scientists have successfully decoded inner speech, or the silent thoughts in a person’s head, with an accuracy rate of up to 74%.This advancement could advance how people with severe speech and motor impairments communicate.
The new brain-computer interface technology can translate a person’s inner thoughts into words, activated only when they “think” a specific mental password.“This is the first time we’ve managed to understand what brain activity looks like when you just think about speaking,” said Erin Kunz, lead author from Stanford University. “For people with severe speech and motor impairments, BCIs capable of decoding inner speech could help them communicate much more easily and more naturally,” Kunz added. Demonstrates 74% accuracy Brain-computer interfaces are not new. It enables direct communication between the brain and external devices.The tech has been used to help people with disabilities control prosthetic limbs by decoding brain signals related to movement. Prior research has shown that brain-computer interfaces can decode attempted speech in people with paralysis by interpreting the brain activity associated with trying to speak. While faster than older methods like eye-tracking systems, using BCIs to decode attempted speech can still be physically demanding and slow for people with limited muscle control.This limitation spurred the Stanford team to investigate the possibility of decoding inner speech – the silent thoughts we have in our heads. “If you just have to think about speech instead of actually trying to speak, it’s potentially easier and faster for people,” said Benyamin Meschede-Krasa, the paper’s co-first author.The study involved four participants with severe paralysis resulting from conditions such as amyotrophic lateral sclerosis or brainstem stroke.The neural activity was recorded using microelectrodes placed in the motor cortex, the part of the brain that controls speech.Participants were instructed to either try to speak or imagine words. Researchers found that both actions activated similar brain regions and produced comparable neural patterns during this activity. However, the brain activity associated with inner speech was notably weaker.But the patterns were distinct enough for artificial intelligence to learn and interpret imagined words.After this, the inner speech data was used to train AI models to decode imagined words. In a demonstration, the BCI could interpret imagined sentences from a vocabulary of up to 125,000 words with an accuracy of up to 74%. The BCI could also pick up on unplanned thoughts, like numbers, when participants counted objects on a screen.Mental password for controlScientists discovered that although attempted speech and inner speech show similar neural patterns, they are distinct enough to be told apart. This difference can be used to train BCIs to ignore inner speech if needed specifically. To give users more control, the team created a mental password-controlled system. This feature lets individuals mentally unlock the BCI’s inner-speech decoding function by thinking of a pre-chosen keyword.Participants successfully used the phrase “chitty chitty bang bang” to initiate the decoding process in their experiments. The system was highly accurate, recognizing the password with over 98% success.Although current BCI technology can’t flawlessly decode spontaneous inner speech, researchers are optimistic. They believe that with more advanced devices, including better sensors and algorithms, BCIs will eventually be able to restore communication that feels as fluent and natural as regular conversation.The findings were published in the journal Cell.
Biology Brain-Computer Interface Health Inner Speech Inventions And Machines Motor Impairments Stanford
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