9 citations · 10 across the 4 of their papers we have counts for
6 papers
Decoding Naturalistic Speech from fMRI with Temporal Modeling and Multimodal Alignment
Corentin Caris, Louis Jalouzot, Nona Rajabi +9
Reconstructing what a person perceives from their brain activity is a stringent test of whether neural recordings carry meaningful information. Naturalistic speech offers a rich te…
Cephalonauts One: A deep fMRI dataset for decoding naturalistic speech in the human brain
Antoine Collas, Louis Jalouzot, Géraud Ilinca +11
Cephalonauts One is a whole-brain 3 Tesla (3T) functional magnetic resonance imaging (fMRI) dataset recorded while subjects listened to audio podcasts. Three healthy subjects under…
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…
Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants
Louis Jalouzot, Alexis Thual, Yair Lakretz +2
We investigate optimal strategies for decoding perceived natural speech from fMRI data acquired from a limited number of participants. Leveraging Lebel et al. (2023)'s dataset of 8…
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…
Aligning individual brains with Fused Unbalanced Gromov-Wasserstein
Alexis Thual, Huy Tran, Tatiana Zemskova +4
Individual brains vary in both anatomy and functional organization, even within a given species. Inter-individual variability is a major impediment when trying to draw generalizabl…