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

The non-invasive pipeline reads a magnetoencephalography signal and reconstructs text without surgery, up from about 48% a year earlier.

- Published: 2026-07-17T05:29:37.841Z
- Canonical: https://polylog.news/ai/2026-07-17/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/), [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/), [The Decoder](https://the-decoder.com/metas-non-invasive-brain-to-text-ai-is-closing-the-gap-with-surgical-implants/)

Meta detailed Brain2Qwerty v2, a non-invasive brain-to-text pipeline that reconstructs typed sentences from magnetoencephalography (MEG) recordings, in a research post. The system reaches an average word accuracy of 61%, rising to 78% for t…

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