Meta AI Releases Brain2Qwerty v2: A Non-Invasive MEG Brain-to-Text Pipeline Decoding Typed Sentences at 61% Word Accuracy

Meta AI just introduced Brain2Qwerty v2 . It decodes natural sentences from non-invasive brain recordings in real time. The system reads magnetoencephalography (MEG) signals while a person types. It reconstructs what they typed, with no implant and no surgery. This is the follow-up to Brain2Qwerty v1, released in February 2025. Meta is also releasing the full training code for both versions. The pipeline combines a convolutional encoder, a transformer, and a character-level language model.

Brain2Qwerty v2 is a brain-to-text decoder. It maps raw brain activity to characters, then to words and sentences.

Meta trained it on approximately 22,000 sentences from nine volunteer participants. Each participant was recorded for 10 hours while actively typing.

Recordings come from a MEG device. MEG measures the magnetic fields produced by neuronal activity, sampled at high temporal resolution.

The model leverages character, word and sentence-level representations. That layered design lets it correct local errors using broader context.

Importantly, this is research, not a product. The decoder is not a consumer device, and it was tested on a small group of volunteers.

The data was collected with Spain’s BCBL (Basque Center on Cognition, Brain and Language). It belongs to that research center.

Earlier non-invasive systems relied on hand-crafted pipelines to detect neural events. Brain2Qwerty v2 replaces that step with end-to-end deep learning.

Per Meta’s repository, the model combines three components: a convolutional encoder, a transformer, and a character-level language model.

The convolutional encoder reads raw MEG signals. It learns features directly from the data instead of using engineered event detectors.

The transformer models longer-range structure across the signal. The character-level language model then constrains the output toward plausible text.

Meta research team describes three ways AI enables the result. Each maps to a concrete engineering decision teams will recognize.

Fine-tuning large language models on neural data adds semantic context. That context bridges noisy brain recordings and coherent language output.

In practice, the language model rejects character sequences that form no real words. It pushes the decoder toward sentences a human would plausibly type.

Here is an illustrative sketch of the published architecture. It mirrors the described components and is not Meta’s exact training code.