UC Davis Hosts BCI Speech Prediction Contest

For the next five months, machine learning gurus can try to best predict the speech of a brain-computer interface (BCI) user who lost the ability to speak due to a neurodegenerative disease. Competitors will design algorithms that predict words from the patient’s brain data. The individual or team whose algorithm makes the fewest errors between predicted sentences and actual attempted sentences will win a US $5,000 prize.

The competition, called Brain-to-text ‘25, is the second-annual public, open-source brain-to-text competition hosted by a research lab part of the BrainGate consortium, which has been pioneering BCI clinical trials since the early 2000s. This year, the competition is being run by the University of California Davis’s Neuroprosthetics Lab. (A group fromStanford University hosted the first competition using brain data from a different BCI user.)

For two years, the UC Davis research team has collected brain data from a 46-year-old man, Casey Harrell, whose speech is unintelligible except to his regular caregivers. Once the speech BCI was trained on Harrell’s brain data, it could decode what he was trying to say over 97 percent of the time and could instantly synthesize his own voice, as previously reported by IEEE Spectrum.

Decoding Speech from Brain Data

Parsing words from brain data is a two-step process: The algorithm must first predict speech sounds, called phonemes, from neural data. Then it must predict words from the phonemes. Competitors will train their algorithms on the brain data corresponding to 10,948 sentences with accompanying transcripts of what Harrell was attempting to say.

Then comes the real test: The algorithms must predict the words in 1,450 sentences from brain data withheld from the training data. The difference between the final set of predicted words and the words that Harrell attempted to say is called the word error rate—the lower the word error rate, the better the speech BCI works, overall.

Researchers reported a 6.70 percent word error rate, which they hope the public can beat. The goal of the competition is to attract machine learning experts who may not realize how valuable their skills are to speech BCIs, says Nick Card, a postdoctoral researcher at UC Davis leading both the clinical trial and the competition.

“We could sit on this data and hide it internally and make more discoveries with it over time,” says Card. “But if the goal is to help make this technology mature faster to help the people who need to benefit from this technology right now, then we want to share it and we want people to help us solve this problem.”

The public invite into the research world is “an awesome development” that is “long overdue” in the BCI space, said Konrad Kording, a professor at the University of Pennsylvania who researches the brain using machine learning, and who is not involved in the research or competition.

This year, Card and his fellow researchers have raised the bar by lowering…