Meta has announced the next version of its AI system for non-invasively decoding brain activity into text. Called Brain2Qwerty v2, the company hopes its latest method will help people with neurological injuries or diseases that impair speech.
Meta’s latest brain-computer interface (BCI) builds off last year’s Brain2Qwerty v1 , which initially showed that non-invasive brain recordings could be decoded into text with surprisingly high character-level accuracy. It used both electroencephalography (EEG) and magnetoencephalography (MEG)—two non-invasive methods that measure the magnetic and electric fields elicited by neuronal activity—although it was only capable of decoding individual characters.
Now, the company has shown off its v2 model , which is said to improve nearly every aspect of the system by using an end-to-end architecture, large language models (LLMs), real-time decoding, and vastly improved pattern recognition.
Note : The findings was presented in a recent paper , which involved researchers from Meta and a host of universities and institutes, including Université PSL (incl. École Normale Supérieure), University of Lille, Paris Cité University, Université Paris-Saclay, CNRS, Inria, CEA (NeuroSpin), Basque Center on Cognition, Brain and Language (BCBL), and Hospital Foundation Adolphe de Rothschild.
According to the paper, Brain2Qwerty v2 was trained on approximately 22,000 sentences from nine volunteer participants, each of which were recorded for 10 hours wearing an MEG device while actively typing.
Meta says that instead of relying on hand-crafted pipelines to detect neural events, they used end-to-end deep learning to decode directly from raw brain signals—essentially meaning they could not only decode single letters like in v1, but also full words and sentences.
While v2 represents a pretty significant leap forward, it hasn’t approached 100 percent accuracy yet:
“Brain2Qwerty v2 recovers sentences coherently from noisy neural inputs, achieving a word accuracy rate of 61%, significantly improving upon the 8% word accuracy from other non-invasive methods,” Meta says.