We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2. Building…

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Summary

Researchers have developed Brain2Qwerty v2, an advanced non-invasive brain-to-text decoder that converts raw brain signals from MEG devices into written sentences with 61% average word accuracy. The system uses deep learning and language models trained on ~22,000 sentences, with the best-performing participant achieving 78% accuracy, and the team is releasing the code and dataset to accelerate neuroscience research.

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#1
We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2. Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication. We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating. 🧵👇
#2
We trained Brain2Qwerty v2 on ~22,000 sentences from 9 volunteers, each recorded for 10 hours wearing an MEG device while typing. By using end-to-end deep learning on raw brain signals from MEG devices and fine-tuning LLMs, the system effectively bridges the gap between noisy neural data and coherent language. The results are promising: - Avg word accuracy of 61% across participants - 78% word accuracy and 50%+ of sentences decoded with ≤ 1 word error for the top-performing participant - Performance scales log-linearly with data volume
#3
To help accelerate neuroscience breakthroughs, we're releasing the full training code for Brain2Qwerty v1 and v2, and our partner, @bcbl_, is releasing the v1 dataset. Learn more and explore the artifacts here: go.meta.me/42ed9c
#4
@bcbl_ For clarification, Brain2Qwerty v1 was published earlier today in @NatureNeuro.