# Meta's Brain2Qwerty v2 Decodes Typed Sentences From Brain Scans at 61% Word Accuracy

A non-invasive magnetoencephalography pipeline, with code and data released, narrows the gap to surgical implants while remaining pure research.

- Published: 2026-07-04T10:44:34.831Z
- Canonical: https://polylog.news/ai/2026-07-04/meta-s-brain2qwerty-v2-decodes-typed-sentences-from-brain-sc
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [Meta AI](https://ai.meta.com/blog/brain2qwerty-brain-ai-human-communication/), [Nature Neuroscience](https://www.nature.com/articles/s41593-026-02303-2), [MarkTechPost](https://www.marktechpost.com/2026/06/30/meta-ai-releases-brain2qwerty-v2-a-non-invasive-meg-brain-to-text-pipeline-decoding-typed-sentences-at-61-word-accuracy/)

Meta released Brain2Qwerty v2, a system that decodes typed sentences from magnetoencephalography (MEG) recordings, a way of reading the brain's magnetic fields without surgery. The pipeline reaches an average 61% word accuracy, with the bes…

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