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Meta Releases Brain2Qwerty v2, a Non-Invasive System Turning Brain Signals Into Sentences

June 29, 2026 at 12:40 EDT

Research & Papers
Foundation Models
Healthcare

On June 29, 2026, Meta unveiled Brain2Qwerty v2, a non-invasive system that reads brain signals from magnetic head activity in real time and converts them into full sentences. According to its official blog, the system decodes raw brain activity measured by MEG (magnetoencephalography) directly through an end-to-end pipeline, producing text at the sentence level. Building on the v1 results released in February 2025, it raises decoding accuracy from the character level to the sentence level (Meta AI blog).

June 29 · Meta AI

Brain2Qwerty v2: typing from thought, no surgery required

Meta's non-invasive brain-to-text system decodes full sentences from raw MEG signals in real time — reaching 61% average word accuracy, a leap over the ~8% of prior non-invasive methods.

Word accuracy vs. prior non-invasive methods

Prior
non-invasive

61%

v2 average

78%

Best participant

Each block ≈ 10% word accuracy on the MEG basis. More than half of sentences had at most one word in error.

volunteers, ~10 hrs of MEG typing each

~22k

sentences collected for training

log-linear

scaling law: accuracy rises with data volume

End-to-end pipeline

Raw MEG signals

→

CNN + Transformer

→

LLM semantic correction

→

Full sentence

v2 drops the hand-crafted signal-processing stage of v1 and applies deep learning directly to raw signals.

Why it matters

No surgery — narrows the gap with invasive systems like Neuralink
Decoding limited to intentional keystrokes
Aimed at restoring communication for paralysis, ALS and aphasia
v1/v2 training code open-sourced

Open questions

MEG is large, costly, cryogenic lab equipment
Subject must stay still — no clear wearable path yet
Research-stage; no large-scale real-user evaluation
Clinical bridging is the next focus

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