PhD student @ Meta & ENS, on Large models and Brains processing language and vision together. https://xitter.cf/t.co/GMLSXoIFGH

Paris, France
Joined October 2021
The EEG/EMG Foundation Challenge 2026 starts in 4 days 💫 🔗 neural-interfaces26.github.i… Four tracks 🧠 : 🖼️ EEG-to-IMG ➗ BCI decoding 😴 Predicting sleep onset ✋ EMG-to-Pose Let's push the frontiers of neural decoding further 🔥
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Joséphine Raugel retweeted
some exciting new work from our AI teams at Meta on non-invasive brain computer interfaces!
We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2. Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication. We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating. 🧵👇
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Brain2Qwerty V2 is out 💫
🧠⌨️ Decode language from brain activity without surgery. 🧠⌨️ Brain2Qwerty V1 is officially published in Nature Neuroscience. Today, we're releasing Brain2Qwerty V2. We achieve unprecedented performance for a non-invasive MEG setup. Details below 🧵👇
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Joséphine Raugel retweeted
We’re happy to announce 2 releases today: - 🧠Brain2qwerty v1 is published at @NatureNeuro - 🚀 Brain2Qwerty v2 is now publicly released Explore how we decode sentences from non-invasive brain recordings: facebookresearch.github.io/b… Links: 📄v1 Nature Neuro: nature.com/articles/s41593-0… 📑v2 Meta preprint: facebookresearch.github.io/b… 💻Code: github.com/facebookresearch/… 📊Data: huggingface.co/datasets/bcbl… 📝Blog: ai.meta.com/blog/brain2qwert… 🧵Thread: xitter.cf/JarodLevy/status/20715…
🧠⌨️ Decode language from brain activity without surgery. 🧠⌨️ Brain2Qwerty V1 is officially published in Nature Neuroscience. Today, we're releasing Brain2Qwerty V2. We achieve unprecedented performance for a non-invasive MEG setup. Details below 🧵👇
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Joséphine Raugel retweeted
We’re very happy to share our latest paper: “Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation“ with up to 68% gain in brain-to-image image decoding! 📝arxiv.org/abs/2606.06345 🧵Details in thread below:
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1/ We’re so glad to share this new study 💫 Does the brain learn like a Deep Net? 🧠⚙️ - 📄Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images - 🔗arxiv.org/abs/2605.28693 Thread below 🧵
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14/ and 🙏to all the open source and open data #NeuroAI communities for making this possible!
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15/ Finally thanks to @ylecun and @pascalefung for the insightful discussions on this topic
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