Meta Brain2Qwerty v2 uses AI to decode brain activity into text without surgery

Meta's Brain2Qwerty v2 turns thoughts into text, and it doesn't need brain implants

The latest AI model decodes brain signals into coherent sentences using external scanners. Artificial intelligence is getting surprisingly good at understanding humans. Now, Meta wants it to understand our brains too. The company has unveiled Brain2Qwerty v2, an upgraded AI system that can translate brain activity into full sentences, all without requiring brain implants or surgery. The goal isn't mind reading for the masses. Instead, it's to help people who have lost the ability to speak communicate again. How a Brain-powered keyboard works The easiest way to think about Brain2Qwerty v2 is as an incredibly advanced brain-powered keyboard. Volunteers wear a Magnetoencephalography (MEG) scanner, which measures tiny magnetic signals produced by the brain while they type. Instead of watching the keyboard, the AI watches those brain signals and predicts what the person intended to type. The biggest leap over the original Brain2Qwerty is that it no longer tries to decode one letter at a time. Instead, it looks at characters, words, and entire sentences, using large language models to fill in the blanks, much like your smartphone predicts the next word while typing. Meta even describes the system as adding semantic understanding, allowing it to recover coherent sentences from extremely noisy brain signals. Recommended Videos Under the hood, the AI combines deep learning models such as Transformers and Convolutional Neural Networks with fine-tuned language models that act almost like a spellchecker for the brain. If the neural signal is incomplete or distorted, the language model uses context to infer what the user most likely intended to say. Meta also used AI agents to optimize the decoding pipeline itself, helping improve real-time performance. As highlighted in the official research paper, the system was trained using around 22,000 typed sentences collected from nine volunteers, each of whom spent roughly 10 hours wearing an MEG scanner while typing. Brain2Qwerty v2 currently achieves an average 61% word accuracy, while the best participant reached 78% accuracy, with more than half of their decoded sentences containing one word error or less. Meta has also open-sourced both the training code and dataset so other researchers can build on the work. The magic of skipping surgery The funny thing is that the biggest breakthrough here isn't the AI. It's the fact that it works without opening someone's skull. Most high-performance brain-computer interfaces today, including Elon Musk's Neuralink, rely on surgically implanted electrodes to achieve high accuracy. Brain2Qwerty v2 takes a very different approach by using a completely external Magnetoencephalography (MEG) scanner to read brain activity, eliminating the risks associated with intracranial implants while still achieving surprisingly strong results. Meta is still a long way from building a consumer product, and nobody should expect to type emails using their thoughts anytime soon. The MEG scanners used by Brain2Qwerty are massive, expensive machines that belong in research labs, not living rooms. But by combining advances in neuroscience with modern AI, Meta is showing that non-invasive brain-computer interfaces may not be as far away as they once seemed. And for people who have lost the ability to communicate, that could end up being far more meaningful than any chatbot or image generator.

Meta Unveils New Tech That Uses AI to Translate Brain Activity Into Text -- Without Surgery

Meta released the training code for Brain2Qwerty v1 and v2, while its research partner is releasing the v1 dataset. Meta on Monday introduced Brain2Qwerty v2, an AI system that translates brain activity into text using non-invasive brain recordings. The company said the research is intended to help people who have lost the ability to communicate because of brain lesions. The system records brain activity using a helmet-like magnetoencephalography (MEG) scanner, a non-invasive brain imaging device commonly used in neuroscience research. It then feeds those raw neural signals into an end-to-end AI model that reconstructs the sentences a person is trying to type. Meta said it further improves accuracy by fine-tuning large language models on neural data, allowing the system to use semantic context when interpreting noisy brain recordings. "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," Meta wrote. "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." Meta said Brain2Qwerty achieved a 61% average word accuracy, compared with roughly 8% for previous non-invasive methods. The company is releasing the system's code and dataset as part of its Digital Brain Project, which also includes a $5 million fund to support open neuroscience datasets. Meta also said decoding accuracy improved as the amount of training data increased, suggesting additional data could further improve performance. The company said AI agents explored possible optimizations for the decoding pipeline before engineers selected the final training configuration. In an accompanying paper published in Nature Neuroscience, Meta researchers argued that while AI has significantly improved brain-to-text decoding, most high-performing brain-computer interfaces still depend on surgically implanted electrodes, making them difficult to scale because of the risks tied to brain surgery and the challenges of maintaining implants over time. Meta said Brain2Qwerty v2 approaches levels of accuracy previously achieved only with techniques requiring brain surgery. The company said its non-invasive approach could help bridge the gap between invasive neuroprosthetics and communication systems that do not require surgery. "Our hope is that this work, done in the open, advances neuroscience to identify, diagnose, and treat neurological disorders faster than in siloes," Meta wrote. The announcement comes as brain-computer interface research accelerates, including by Elon Musk's Neuralink and Merge Labs, backed by OpenAI CEO Sam Altman, developing technology to help restore communication for people with neurological disorders. While companies such as Neuralink and Synchron are pursuing implanted interfaces that require surgery, a growing number of researchers and startups are using AI to improve the performance of non-invasive systems. In September 2024, startup Neurable introduced AI-powered EEG headphones designed to monitor focus and cognitive fatigue. A year later, MIT spinout AlterEgo unveiled a wearable that converts silent neuromuscular signals from the face and throat into text and commands, positioning it as a practical alternative to implanted brain-computer interfaces. Meta did not immediately respond to a request for comment by Decrypt.