# Meta's Brain2Qwerty Decodes Typed Sentences From Non-Invasive Brain Scans at 61 Percent Word Accuracy

The second version reads magnetoencephalography signals as volunteers type, a large improvement over prior surgery-free methods, but the scanner is not wearable and error rates remain too high for daily use.

- Published: 2026-07-25T05:43:03.754Z
- Canonical: https://polylog.news/ai/2026-07-25/meta-s-brain2qwerty-decodes-typed-sentences-from-non-invasiv
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [AI at Meta](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/)

Meta has detailed Brain2Qwerty, a deep-learning pipeline that decodes typed sentences from non-invasive brain recordings while participants type memorized sentences on a keyboard. The system reads magnetoencephalography (MEG), which measure…

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