The team led by Anne-Lise Giraud at the University of Geneva (UNIGE) shows that individual training improves brain-machinedecoding of imagined speech, offering new hope for people with language disorders.
Brain-machine interfaces have the potential to transform care for individuals who are unable to speak. However, decoding internal language remains highly challenging due to the low-amplitude brain signals involved. By training volunteers to imagine specific syllables, the team lead by Anne-Lise Giraud, director if the Hearing Institute and reConnect Institute, at the University of Geneva used machine learning algorithms to successfully decode the corresponding signals in real time. The study shows that personalized training can help individuals control these interfaces more effectively, while also identifying the brain regions involved in this improvement.
Published in Communications Biology, this research paves the way for practical applications for people with aphasia.
Bhadra, K., Giraud, A.-L., & Marchesotti, S. (2025). Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity. Communications Biology, 8(1), 1‑15. https://doi.org/10.1038/s42003-025-07464-7