# Meta debuts non-invasive Brain2Qwerty v2 decoder > Source: > Published: 2026-06-30 04:47:28+00:00 For practitioners: Progress in non-invasive brain-to-text decoding changes the data and modeling trade-offs for BCI research, increasing the importance of large annotated neural-language corpora and robust transfer techniques. Meta published a research paper and engineering blog describing **Brain2Qwerty v2**, a non-invasive pipeline that decodes intended typed sentences from magnetoencephalography (MEG) recordings. According to Meta's paper and blog, the team trained on about **22,000** sentences collected from **nine** volunteers who each spent roughly **10** hours wearing an MEG device; the system uses end-to-end deep learning with fine-tuned large language models and agent-driven configuration search. Meta reports an average **61%** word accuracy across participant-specific models (best single participant: **78%**), versus about **8%** for prior non-invasive approaches, and frames this as approaching accuracies previously seen only with invasive recordings (per Meta research and the company blog).