Pooling patient brain signals could speed brain-to-speech devices, human trial finds
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Stroke, ALS and other neurodegenerative and acute brain disorders can rob people of the ability to speak. Brain-computer interfaces can restore it by translating brain activity into machine-generated speech. But they rely on patient-by-patient data collection, a major barrier to making these devices practical and widely available.
Duke researchers are making these systems faster and easier to deploy. In a study published in Nature Communications, researchers demonstrated—for the first time in humans—that pooling data across patients can work rather than relying on fully individualized training. The team was led by Greg Cogan, Ph.D., associate professor of neurology, and Jonathan Viventi, Ph.D., Hawkes Family Associate Professor of Biomedical Engineering.
By using high-resolution recordings (micro-ECoG recordings) from awake neurosurgical patients, they aligned brain activity across individuals to train a shared decoder, improving performance while reducing the amount of data needed from each patient.
"In our study, a working decoder could be built from as little as five minutes of the new patient's own recordings, supplemented by aligned data from others," Cogan said.
It's an encouraging step toward making these systems more scalable and accessible.
Publication details
Z. Spalding et al, Shared latent representations of speech production for cross-patient speech decoding, Nature Communications (2026). DOI: 10.1038/s41467-026-75455-1
Journal information: Nature Communications
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