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Meta releases Brain2Qwerty v2: non-invasive brain-to-text decoding at 61% word accuracy

Meta AI released Brain2Qwerty v2, an end-to-end deep learning pipeline that decodes text from non-invasive magnetoencephalography (MEG) brain recordings in real time, achieving 61% word accuracy — up from 8% for prior non-invasive methods and approaching surgical-implant performance. The system was trained on ~22,000 sentences from nine participants and uses fine-tuned large language models on neural data to bridge noisy brain signals and coherent language. Meta is releasing full training code for both v1 and v2, and partner institution BCBL is releasing the v1 dataset. The work is part of Meta's broader Digital Brain Project and open neuroscience initiative.