27 citations · 41 across the 14 of their papers we have counts for
15 papers
Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings
Mingfang Zhang, Jarod Lévy, Cedric Rommel +9
Restoring communication for people who have lost the ability to speak or move after a brain injury is a major challenge. While intracranial implants now enable high-performing brai…
Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation
Yohann Benchetrit, Marlène Careil, Simon Dahan +3
Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain deco…
Polar probe linearly decodes semantic structures from LLMs
Pablo J. Diego-Simón, Pierre Orhan, Emmanuel Chemla +2
How do artificial neural networks bind concepts to form complex semantic structures? Here, we propose a simple neural code, whereby the existence and the type of relations between…
DANCE: Detect and Classify Events in EEG
Jarod Lévy, Hubert Banville, Jérémy Rapin +3
Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned to known event onsets. However,…
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
Hubert Banville, Stéphane d'Ascoli, Simon Dahan +12
Deep learning and large public datasets have recently catalyzed the proliferation of AI models for processing brain recordings. However, systematically evaluating these models rema…
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…
Decoding individual words from non-invasive brain recordings across 723 participants
Stéphane d'Ascoli, Corentin Bel, Jérémy Rapin +4
Deep learning has recently enabled the decoding of language from the neural activity of a few participants with electrodes implanted inside their brain. However, reliably decoding…
A polar coordinate system represents syntax in large language models
Pablo Diego-Simón, Stéphane D'Ascoli, Emmanuel Chemla +2
Originally formalized with symbolic representations, syntactic trees may also be effectively represented in the activations of large language models (LLMs). Indeed, a 'Structural P…
Aligning brain functions boosts the decoding of visual semantics in novel subjects
Alexis Thual, Yohann Benchetrit, Felix Geilert +4
Deep learning is leading to major advances in the realm of brain decoding from functional Magnetic Resonance Imaging (fMRI). However, the large inter-subject variability in brain c…
Brain decoding: toward real-time reconstruction of visual perception
Yohann Benchetrit, Hubert Banville, Jean-Rémi King
In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded…