Scientists say new brain-computer interface allows users to transmit 62 words per minute

Scientists say new brain-computer interface allows users to transmit 62 words per minute

A team of Stanford scientists claim to have tested a new brain-computer interface (BCI) that can decode speech at up to 62 words per minute, improving the previous record by 3.4 times.

That would be a big step toward real-time speech conversion at the rate of natural human conversation.

Max Hodak, who co-founded the BCI company Neuralink with Elon Musk but was not involved in the study, called the research “a significant change in the usefulness of implanted BCIs” in an email to Futurism.

As detailed in a paper that has yet to be peer-reviewed, the Stanford team of scientists found that they only needed to analyze brain activity in a relatively small region of the cortex to convert it into coherent speech using a machine learning algorithm.

The goal was to give voice back to those who can no longer speak due to ALS. While keyboard-based solutions have allowed paralyzed people to communicate again to some degree, a brain-based voice interface could significantly speed up decoding.

“Here, we demonstrate a voice BCI that can decode unrestrained sentences from a large vocabulary at a rate of 62 words per minute, the first time that a BCI has far exceeded the communication rates that alternative technologies can provide to people.” with paralysis, for example, eye tracking,” the researchers write.

In one experiment, the team recorded the neural activity of an ALS patient, who can move his mouth but has difficulty forming words, from two small areas of the brain.

Using a recurrent neural network decoder that can predict text, the researchers turned these signals into words, and at a surprisingly fast rate.

They found that analyzing these orofacial movements and their associated neural activity was “probably strong enough to support a BCI of speech, despite paralysis and narrow cortical surface coverage,” according to the article.

But the system was not perfect. The error rate of the researchers’ recurrent neural network (RNN) decoder was still around 20 percent.

“Our demonstration is proof of concept that decoding speech movement intents from intracortical recordings is a promising approach, but not yet a complete and clinically viable system,” the researchers admitted in their paper.

To improve the error rate of their system, the scientists propose to probe more areas of the brain and, at the same time, optimize the algorithm.

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