Meta Releases Brain2Qwerty v2: Non-Invasive MEG Brain-to-Text at 61% Word Accuracy

Meta AI / MarkTechPost·medium signal

Meta AI published Brain2Qwerty v2 (with a Nature paper), a real-time end-to-end pipeline that decodes typed sentences from non-invasive MEG brain recordings — trained on ~22,000 sentences from nine participants over 10-hour sessions, hitting 61% average word accuracy (78% for the best participant). Crucially for builders and researchers, Meta open-sourced the v1 and v2 training code (github.com/facebookresearch/brain2qwerty) and partner BCBL is releasing the v1 dataset, approaching accuracy previously requiring surgical implants.