How accurate is Brain2Qwerty v2 at decoding sentences?

Brain2Qwerty v2 reaches 61% average word accuracy across nine participants, with the best participant hitting 78% and more than half of that participant's sentences decoded with one word error or less. Meta reports word error rate improves log-linearly with training data, so the gap to surgical implants is expected to narrow as more MEG data is collected.

Does Brain2Qwerty v2 require brain surgery?

Brain2Qwerty v2 is fully non-invasive: it reads brain activity through a magnetoencephalography (MEG) scanner, the same hospital-grade equipment used in clinical neuroscience, not implanted electrodes. Meta frames this as the first time non-invasive decoding approaches accuracies that had been exclusive to surgical brain-computer interfaces.

How does Brain2Qwerty v2 work under the hood?

Brain2Qwerty v2 swaps the hand-tuned event detection in v1 for an end-to-end deep network: a convolutional encoder feeds a transformer that is decoded by a character-level language model fine-tuned on neural data. Meta also used AI agents to search the pipeline configuration space, with engineers picking the final setup.

Where can I get the Brain2Qwerty v2 code and data?

Meta released the full training code for both v1 and v2 on GitHub at facebookresearch/brain2qwerty, alongside a peer-reviewed Nature Neuroscience paper and a research publication page on ai.meta.com. The earlier Spanish MEG/EEG corpus from the Basque Center on Cognition, Brain and Language is on Hugging Face under bcbl190626/SpanishBCBL.

What's actually new in v2 versus Brain2Qwerty v1?

Brain2Qwerty v1, shared in early 2025, decoded brain activity into text but with a much higher error rate. The v2 paper replaces manual event detection with end-to-end deep learning, fine-tunes a large language model on the neural signal for semantic context, and adds AI-agent-assisted pipeline search — together pushing accuracy from 8% to 61%.

Meta AI Research · 2026-06-29 · major

Brain2Qwerty v2 — Meta non-invasive brain-to-text hits 61% word accuracy

Brain2Qwerty v2 decodes typed sentences from MEG brain recordings at 61% word accuracy, up from 8% for prior non-invasive methods. Meta trained the model on 22,000 sentences from 9 volunteers, with code and the paper now public.

Brain2Qwerty GitHub repository — Meta AI's non-invasive brain-to-text decoder

Meta's Brain2Qwerty v2 reads typed sentences off MEG brain scans at 61% word accuracy — without surgery.

Quick facts

Maker	Meta AI Research (FAIR)
Version	Brain2Qwerty v2
Word accuracy	61% average, 78% best participant
Prior non-invasive baseline	8%
Training data	22,000 sentences from 9 volunteers (10h MEG each)
Code	github.com/facebookresearch/brain2qwerty
Published	Nature Neuroscience + ai.meta.com blog

What is it?

Brain2Qwerty v2 is Meta AI's second-generation pipeline for decoding what a person is typing directly from non-invasive brain recordings. It pairs a magnetoencephalography (MEG) headset with a deep neural decoder, so the volunteer never has electrodes implanted and never has to speak aloud. The release covers both the trained model and the surrounding research code.

How does it work?

Raw MEG signals flow into a convolutional encoder, then a transformer, then a character-level language model that Meta fine-tunes on the neural data so it can use linguistic context to fix noisy character predictions. Compared with Brain2Qwerty v1, the v2 pipeline drops hand-coded event detection in favor of fully end-to-end training and uses LLM-based AI agents to search the configuration space — final hyperparameters were picked by the engineers.

Why does it matter?

Until now, only surgical brain-computer interfaces could decode typed sentences at workable accuracy; non-invasive methods sat near 8%. Brain2Qwerty v2 lifts that to 61% average (78% for the best participant), with accuracy improving log-linearly with more recording data. That puts no-surgery brain-to-text on the same trajectory as implanted BCIs, which matters for people who can't undergo neurosurgery.

Who is it for?

neuroscience and BCI researchers, accessibility engineers

Frequently asked questions

How accurate is Brain2Qwerty v2 at decoding sentences?: Brain2Qwerty v2 reaches 61% average word accuracy across nine participants, with the best participant hitting 78% and more than half of that participant's sentences decoded with one word error or less. Meta reports word error rate improves log-linearly with training data, so the gap to surgical implants is expected to narrow as more MEG data is collected.
Does Brain2Qwerty v2 require brain surgery?: Brain2Qwerty v2 is fully non-invasive: it reads brain activity through a magnetoencephalography (MEG) scanner, the same hospital-grade equipment used in clinical neuroscience, not implanted electrodes. Meta frames this as the first time non-invasive decoding approaches accuracies that had been exclusive to surgical brain-computer interfaces.
How does Brain2Qwerty v2 work under the hood?: Brain2Qwerty v2 swaps the hand-tuned event detection in v1 for an end-to-end deep network: a convolutional encoder feeds a transformer that is decoded by a character-level language model fine-tuned on neural data. Meta also used AI agents to search the pipeline configuration space, with engineers picking the final setup.
Where can I get the Brain2Qwerty v2 code and data?: Meta released the full training code for both v1 and v2 on GitHub at facebookresearch/brain2qwerty, alongside a peer-reviewed Nature Neuroscience paper and a research publication page on ai.meta.com. The earlier Spanish MEG/EEG corpus from the Basque Center on Cognition, Brain and Language is on Hugging Face under bcbl190626/SpanishBCBL.
What's actually new in v2 versus Brain2Qwerty v1?: Brain2Qwerty v1, shared in early 2025, decoded brain activity into text but with a much higher error rate. The v2 paper replaces manual event detection with end-to-end deep learning, fine-tunes a large language model on the neural signal for semantic context, and adds AI-agent-assisted pipeline search — together pushing accuracy from 8% to 61%.

Try it

git clone https://github.com/facebookresearch/brain2qwerty