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A man with paralysis has used his thoughts to create text

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Researchers at Stanford University have successfully implanted a brain-computer interface capable of interpreting thoughts of handwriting.

The latest advance in brain-computer interfaces (BCI) has converted a person’s thoughts about writing words into text. Using an artificial neural network, researchers at Stanford University in California successfully converted the brain signals of a 65-year-old man paralysed below the neck due to a spinal cord injury.

AI software was coupled with the BCI device, which was implanted in the man’s brain. The software was able to decode information from the BCI to quickly convert the man’s thoughts about handwriting into text on a computer screen.

He was asked to imagine writing letters and words on a piece of paper, and eventually was able to generate 90 characters a minute as the implant picked up his attempts at handwriting.

“This approach allowed a person with paralysis to compose sentences at speeds nearly comparable to those of able-bodied adults of the same age typing on a smartphone,” said Dr Jaimie Henderson, professor of neurosurgery at Stanford.

“The goal is to restore the ability to communicate by text.”

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The team’s paper, published in Nature, says that to the researchers’ 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 the participant, which is around 115 characters per minute.

The device was 94pc accurate in its conversions, jumping to 99pc when the researchers introduced an autocorrect tool.

It was also faster than typical head or eye-tracking systems, which allow users to move a cursor to type messages. These systems can also have drawbacks, according to Henderson.

“If you’re using eye tracking to work with a computer, then your eyes are tied to whatever you’re doing,” he told New Scientist. “You can’t look up or look around or do something else. Having that additional input channel could be really important.”

While the model used in this study is specific to the test subject and won’t translate to other people, the researchers are planning to develop the technology further. The goal is to create tech for people who have lost the use of their upper limbs or the ability to speak.

“Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis,” the research paper says.

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