Machine Learning Contest Aims to Improve Speech BCIs
IEEE Spectrum - AI
Aug 16, 2025 13:00
Elissa Welle
1 views
airesearchieeetechnology
Summary
A new five-month machine learning competition, Brain-to-text ‘25, challenges participants to develop algorithms that accurately predict speech from brain-computer interface (BCI) data collected from a patient unable to speak due to a neurodegenerative disease. Hosted by UC Davis’s Neuroprosthetics Lab as part of the BrainGate consortium, the contest aims to advance open-source BCI technology and improve AI-driven speech decoding for individuals with severe communication impairments. The initiative highlights the growing role of AI in medical neurotechnology and the potential for collaborative innovation in assistive communication.
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 from Stanford 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 unin