We have a new postdoc position to work on Neuro AI and human intracranial recording with Pierre Bourdillon and I: Apply here: docs.google.com/forms/d/e/1F... Lab info: bourdillon-titan-lab.fr feel free to RT ;)
Who's Who of AI
Jean-Rémi King
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Researcher with public evidence across AI research.
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Researcher in Neuroscience & AI
CNRS, Ecole Normale Supérieure, PSL
currently detached to Meta
What they're sharing
docs.google.com
GitHub - facebookresearch/brain2qwerty: Non-invasive decoding of typed sentences from MEG and EEG brain recordings using a convolutional encoder, transformer, and character-level language model. github.com
From Brain Waves to Words: Brain2Qwerty Offers a New Path to Communication Without Surgery ai.meta.com
bcbl190626/SpanishBCBL · Datasets at Hugging Face huggingface.co
Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation arxiv.org
TITAN – AI & Neuroscience Research Lab – Translational Innovations and Technologies in Artificial-intelligence and Neuroscience bourdillon-titan-lab.fr
Articles & links
📄v1 Nature Neuro: www.nature.com/articles/s41... 📑v2 Meta preprint: facebookresearch.github.io/brain2qwerty... 💻Code: github.com/facebookrese... 📊Data: huggingface.co/datasets/bcb... 📝Blog: ai.meta.com/blog/brain2q...
AI Weekly's analysis
→
- Meta's FAIR lab released Brain2Qwerty v2, a non-invasive MEG-to-text pipeline reaching an average 61% word accuracy across nine volunteers.
- The system was trained on roughly 22,000 sentences per participant recorded over 10 hours, with the top participant reaching 78% word accuracy.
- The original Brain2Qwerty study, run with 35 volunteers, is being published in Nature Neuroscience with a v1 MEG character error rate of 32%.
Read full analysis →
📄v1 Nature Neuro: www.nature.com/articles/s41... 📑v2 Meta preprint: facebookresearch.github.io/brain2qwerty... 💻Code: github.com/facebookrese... 📊Data: huggingface.co/datasets/bcb... 📝Blog: ai.meta.com/blog/brain2q...
AI Weekly's analysis
→
- Brain2Qwerty v2 reaches 61% word accuracy decoding typed sentences from MEG recordings, up from roughly 8% for prior non-invasive methods.
- The system was trained on around 22,000 sentences from nine volunteer participants, each wearing MEG for 10 hours while actively typing.
- Meta is releasing full training code for v1 and v2, alongside a Basque Center dataset and a $5 million Digital Brain Project open-data fund.
Read full analysis →
📄v1 Nature Neuro: www.nature.com/articles/s41... 📑v2 Meta preprint: facebookresearch.github.io/brain2qwerty... 💻Code: github.com/facebookrese... 📊Data: huggingface.co/datasets/bcb... 📝Blog: ai.meta.com/blog/brain2q...
🧠➜🖼️ Boost brain-to-image decoding (up to 68% 🚀!) by adding synthetic brain data (#TribeV2) in the training set. Our latest paper is out: arxiv.org/abs/2606.06345
We have a new postdoc position to work on Neuro AI and human intracranial recording with Pierre Bourdillon and I: Apply here: docs.google.com/forms/d/e/1F... Lab info: bourdillon-titan-lab.fr feel free to RT ;)
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