# Meta's Brain2Qwerty v2 Decodes Typed Sentences From Brain Signals at 61 Percent Word Accuracy

The non-invasive system reads magnetoencephalography rather than implants, closing part of the gap with surgical interfaces while still requiring a room-scale scanner.

- Published: 2026-07-15T05:32:00.431Z
- Canonical: https://polylog.news/ai/2026-07-15/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/), [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's Brain2Qwerty v2 decodes sentences that a person types from memory by reading brain activity, and does so without surgery. Using magnetoencephalography (MEG), the system reaches an average character error rate of 29 percent and an ave…

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