2 citations · 2 across the 3 of their papers we have counts for
5 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…
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…
Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation
Paul A. Taylor, Himanshu Aggarwal, Peter Bandettini +39
Visualizations are vital for communicating scientific results. Historically, neuroimaging figures have only depicted regions that surpass a given statistical threshold. This practi…
Across-subject ensemble-learning alleviates the need for large samples for fMRI decoding
Himanshu Aggarwal, Liza Al-Shikhley, Bertrand Thirion
Decoding cognitive states from functional magnetic resonance imaging is central to understanding the functional organization of the brain. Within-subject decoding avoids between-su…