Excited to be with my team at #ccn2025 this week! I’ll be presenting part of the workshop on Thursday. Come say hi!

🔥”Brain-to-Text Decoding” is now out on ArXiv: arxiv.org/abs/2502.17480
Our paper from AI at Meta and @bcbl.bsky.social presents Brain2Qwerty, an AI model that decodes text from non-invasive recordings of the brain.

Below a detailed thread 🧵1/7

This research was made possible by our great team @jarodlevy.bsky.social, Stéphane d'Ascoli, Jérémy Rapin, F.-Xavier Alario, Pierre Bourdillon, Svetlana Pinet, @jeanremiking.bsky.social at AI at Meta, @bcbl.bsky.social, @cnrs.fr , @psl-univ.bsky.social, and Hôpital Fondation Rothschild!

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Interested in efficiently decoding these brain signals? Go check out our companion AI paper:
ai.meta.com/research/pub...

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Result 4: This dynamic code is observed for all levels of the language hierarchy. Critically, it is level-dependent: context representations “move” more slowly in brain activity than letter representations, allowing a seamless unfolding of language representations.

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Result 3:
How does the brain avoid the interference induced by such overlapping representations?

Thanks to a dynamic code! The representations of successive letters continuously move across different neural subspaces.

Result 2: Paradoxically, the representations of letters last much longer than their respective corresponding actions, resulting in a representational overlap of successive letters in brain activity.

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Result 1: We find that, before typing each word, the brain activity is marked by a top-down sequence of representations: context-level representations can be decoded before those of words, syllables, and letters.

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Method: We used MEG to record the brain activity of participants while they typed sentences.

Using linear decoding, we then evaluate whether the brain represents a hierarchy of linguistic features before each word is typed.

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Our paper from AI at Meta and @bcbl.bsky.social is out on arxiv 🔥
“From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production”
arxiv.org/abs/2502.07429

How does the brain transform a thought into a sequence of motor actions?

Results summarized in 🧵1/8

Result 2: Paradoxically, the representations of letters last much longer than their respective corresponding actions, resulting in a representational overlap of successive letters in brain activity.

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Result 1: We find that, before typing each word, the brain activity is marked by a top-down sequence of representations: context-level representations can be decoded before those of words, syllables, and letters.

3/8

Method: We used MEG to record the brain activity of participants while they typed sentences.

Using linear decoding, we then evaluate whether the brain represents a hierarchy of linguistic features before each word is typed.

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