10 papers
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…
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…
NeuralSet: A High-Performing Python Package for Neuro-AI
Jean-Rémi King, Corentin Bel, Linnea Evanson +25
Artificial intelligence (AI) is increasingly central to understanding how the brain processes information. However, the integration of neuroscience and modern AI is bottlenecked by…
A foundation model of vision, audition, and language for in-silico neuroscience
Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit +5
Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. He…
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…