# Meta's Non-Invasive Brain-to-Text System Decodes Typed Sentences From Magnetic Signals

Brain2Qwerty reaches a 32 percent character error rate using magnetoencephalography (MEG) and 19 percent for the best participants, with no surgery, though electroencephalography (EEG) remains far worse at 67 percent.

- Published: 2026-07-11T06:01:51.690Z
- Canonical: https://polylog.news/ai/2026-07-11/meta-s-non-invasive-brain-to-text-system-decodes-typed-sente
- 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 detailed Brain2Qwerty, a deep-learning system that decodes sentences from brain activity while participants type memorized text, using either electroencephalography (EEG) or magnetoencephalography (MEG). Across a group of 35 volunteers…

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