Meta’s Brain2Qwerty v2 reconstructs text typed silently from continuous brain signals without exact keypress timing, reaching 61% word accuracy on average and about 70% with top users.

It processes raw MEG data through an encoder, an Aligner detecting word boundaries and embeddings, then a large language model refines the output. The system costs millions and isn’t consumer-ready.

Accuracy remains low for practical use, but a near-perfect scaling law shows quality improves almost logarithmically with more data, with no saturation yet. Noninvasive thought reading may just need bigger datasets.