27 citations · 41 across the 14 of their papers we have counts for
6 papers · 1 filter
From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining
Linnea Evanson, Mingfang Zhang, Hubert Banville +3
Decoding speech from brain activity has typically relied on limited neural recordings collected during short and highly controlled experiments. Here, we introduce a framework to le…
TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction
Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit +2
Historically, neuroscience has progressed by fragmenting into specialized domains, each focusing on isolated modalities, tasks, or brain regions. While fruitful, this approach hind…
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel +16
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-ba…
Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI
Marlène Careil, Yohann Benchetrit, Jean-Rémi King
Brain-to-image decoding has been recently propelled by the progress in generative AI models and the availability of large ultra-high field functional Magnetic Resonance Imaging (fM…
Brain-to-Text Decoding: A Non-invasive Approach via Typing
Jarod Lévy, Mingfang Zhang, Svetlana Pinet +4
Modern neuroprostheses can now restore communication in patients who have lost the ability to speak or move. However, these invasive devices entail risks inherent to neurosurgery.…
Scaling laws for decoding images from brain activity
Hubert Banville, Yohann Benchetrit, Stéphane d'Ascoli +2
Generative AI has recently propelled the decoding of images from brain activity. How do these approaches scale with the amount and type of neural recordings? Here, we systematicall…