
Meta's brain-to-text AI achieves 61% accuracy, open-source code released simultaneously
Meta this week released Brain2Qwerty v2, a non-invasive brain-computer interface system that records neural activity through a helmet-style MEG (magnetoencephalography) scanner and directly decodes target text using an end-to-end deep learning model, achieving an average word accuracy of 61%. Meta has also open-sourced the code and dataset as part of its Digital Brain Project, and established a $5 million fund. Brain2Qwerty v2 Technical Architecture: Training Scale and End-to-End Decoding Approa








