# Meta's Brain2Qwerty v2 Decodes Typed Sentences From Brain Signals at 61 Percent Word Accuracy, Without Surgery

The non-invasive pipeline lifts accuracy from 8 percent for prior methods, but still depends on a room-sized magnetoencephalography scanner.

- Published: 2026-07-16T05:44:01.303Z
- Canonical: https://polylog.news/ai/2026-07-16/meta-s-brain2qwerty-v2-decodes-typed-sentences-from-brain-si
- 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/)

Meta detailed Brain2Qwerty, a non-invasive brain-to-text system that reconstructs typed sentences from magnetoencephalography (MEG) signals recorded while a person types. The second version reaches 61 percent word accuracy on average, with…

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