What it is
Meta AI trained a deep-learning architecture, Brain2Qwerty, to reconstruct sentences from the brain activity of 35 healthy volunteers as they typed briefly memorized text. Using magnetoencephalography (MEG), the model reached an average character-error rate of 32%, far ahead of the 67% rate it managed with electroencephalography (EEG). For the best participant, the error rate fell to 19%, low enough to perfectly decode a range of sentences it had never seen during training.
Why it matters
Brain-to-text systems that reach usable accuracy today require electrodes implanted on the cortex, which carries the risks inherent to neurosurgery. Brain2Qwerty shows that a fully non-invasive recording can decode language, narrowing the gap to implanted devices: the best-participant 19% error rate begins to approach practical territory. Because MEG so decisively beat EEG (32% versus 67% error), the work also signals which sensing technology safe future BCIs should be built around.
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Filed underbrain-computer interface, MEG, deep learning, neurotechnology, non-invasive