Meta has published Brain2Qwerty v2, a non-invasive system that reads brain activity while a person types and reconstructs the intended sentence. Trained on roughly 22,000 sentences from nine volunteers, the model averages 61% word accuracy, compared with about 8% for earlier non-invasive baselines, putting it close to accuracy levels that previously required surgical electrodes. Meta released the code for v1 and v2 and is publishing the dataset through its Digital Brain Project, alongside a $5 million fund for open neuroscience data.
What changed in v2
Brain2Qwerty v2 is built around an end-to-end deep learning pipeline that ingests raw magnetoencephalography (MEG) signals, rather than relying on hand-crafted feature extractors. MEG captures the magnetic field produced by neuronal activity using a helmet-style scanner placed over the head, with no implants involved.
Two design choices drove the jump in accuracy. First, Meta moved from modular pipelines to a single end-to-end model trained directly on neural recordings. Second, the system leans on large language models fine-tuned on neural data, which lets the decoder use semantic context to recover words that the MEG signal picks up only weakly or noisily.
Meta described the approach in plain terms: instead of relying on hand-crafted pipelines to detect neural events, the team uses end-to-end deep learning to decode directly from raw brain signals.
How accurate is it, and on what data
The reported numbers come from a controlled typing setup. Nine volunteers wore the MEG helmet while actively typing, contributing about 22,000 training sentences and roughly 10 hours of recorded data per participant. Under those conditions, the model reached 61% average word accuracy. Earlier non-invasive systems sat near 8% on comparable tasks.
Meta notes that accuracy kept improving as more training data was added, which points to a straightforward lever for future gains: scale the dataset.
What Meta is releasing and where
The work is published in Nature Neuroscience. Meta is also releasing the v1 and v2 code, and a research partner has published the v1 dataset. The release sits inside the broader Digital Brain Project, which includes a $5 million fund aimed at building open neuroscience datasets that other groups can train on.
Why a non-invasive brain-to-text system matters
Most high-accuracy brain-computer interfaces still depend on electrodes implanted in the brain. Surgery limits who can use the technology, adds clinical risk, and makes long-term maintenance harder. A helmet-based system that approaches the accuracy of implanted arrays removes the single biggest practical barrier.
Meta frames the target population as people who have lost the ability to communicate because of brain lesions, where regaining everyday speech or typing is the primary goal. The same architecture could also seed consumer-facing wearables, hands-free interfaces, or assistive tools that read typing intent without any implanted hardware.
How Brain2Qwerty v2 compares with the rest of the field
The announcement lands in a crowded landscape. Neuralink and Synchron continue to pursue implanted interfaces. Merge Labs, backed by OpenAI chief Sam Altman, is working on its own technology. On the non-invasive side, Neurable shipped AI-powered EEG headphones in 2024, and MIT spinout AlterEgo has shown a wearable that turns silent signals from the face and throat into text.
Meta’s contribution is the accuracy jump on a fully non-invasive setup, plus a public release of code and data that other labs can build on rather than a closed product.
Open questions and limits
61% word accuracy is a major step up from near-random baselines, but it is still well short of the near-100% accuracy that fluent typing requires. The current setup also depends on a shielded MEG environment, which is not something a consumer can wear on a commute. Practical deployment will need cheaper, more portable sensors and more training data, which is one reason Meta is funding open datasets.
Meta also disclosed that AI agents were used to search for optimisations in the decoding pipeline before engineers finalized the configuration, a small signal that automated research tooling is starting to influence how these systems are designed.
FAQ
What is Brain2Qwerty v2?
Brain2Qwerty v2 is Meta’s non-invasive AI system that decodes brain activity into text. It records neural signals with a helmet-like MEG scanner and uses an end-to-end deep learning model, supported by fine-tuned language models, to reconstruct the sentences a person is trying to type.
How accurate is Brain2Qwerty v2?
Meta reports 61% average word accuracy on the test setup, versus roughly 8% for prior non-invasive methods. Meta says this approaches accuracy levels that earlier work achieved only with surgically implanted electrodes.
Does Brain2Qwerty v2 require surgery?
No. The system uses an external MEG helmet and reads brain activity from outside the skull. Meta has released the v1 and v2 code and is publishing the work through its Digital Brain Project, which includes a $5 million fund for open neuroscience datasets.