Meta's brain-scanning system reads sentences non-invasively, code open source
Key takeaways
- To help accelerate neuroscience breakthroughs, we're releasing the full training code for Brain2Qwerty v1 and v2, and our partner, the Basque Center on Cognition, Brain, and Language (BCBL), is releasing the v1 dataset.
- We trained Brain2Qwerty v2 on approximately 22,000 sentences from nine volunteer participants, each recorded for 10 hours wearing a magnetoencephalography (MEG) device while actively typing.
- Fine-tuning large language models on neural data allows the system to leverage semantic context, bridging the gap between noisy brain recordings and coherent language.
Research From Brain Waves to Words: Brain2Qwerty Offers a New Path to Communication Without Surgery June 29, 2026•3 minute read Last year, we introduced Brain2Qwerty v1, research that uses AI to decode brain activity into text without any surgical implant. Now we're sharing the next step: Brain2Qwerty v2, the highest-performing end-to-end pipeline capable of real-time sentence decoding from non-invasive brain recordings, approaching levels of accuracy previously exclusive to techniques that require brain surgery.
To help accelerate neuroscience breakthroughs, we're releasing the full training code for Brain2Qwerty v1 and v2, and our partner, the Basque Center on Cognition, Brain, and Language (BCBL), is releasing the v1 dataset. We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions that prevent them from communicating. Invasive procedures like stereotactic electroencephalography and electrocorticography have shown that a neuroprosthesis feeding signals to an AI decoder can restore communication, but they're difficult to scale. Our noninvasive approach can help bridge that gap.
We trained Brain2Qwerty v2 on approximately 22,000 sentences from nine volunteer participants, each recorded for 10 hours wearing a magnetoencephalography (MEG) device while actively typing. Instead of relying on hand-crafted pipelines to detect neural events, we use end-to-end deep learning to decode directly from raw brain signals.