arXiv：cs.LG· Antoine Collas, Louis Jalouzot, G\'eraud Ilinca, Corentin Caris, Romain Valabr\`egue, Ahmed Hassayoune, David Goncalves, Madeleine Hueber, Thadd\'ee Delebarre, Julien Savatovsky, Clara Fonteneau, Charles Maussion, Bertrand Thirion, Alexis Thual·· 3 小时前AI 评分49
Cephalonauts One：用于解码人脑自然语音的深度 fMRI 数据集
Cephalonauts One: A deep fMRI dataset for decoding naturalistic speech in the human brain
AI 导读
Cephalonauts One 是一个全脑 3T fMRI 数据集，记录了三名健康受试者收听母语播客时的脑活动，每位受试者拥有 30 小时 fMRI 数据，是当前使用自然语音刺激的最深 fMRI 数据集。
正文
Authors:Antoine Collas, Louis Jalouzot, Géraud Ilinca, Corentin Caris, Romain Valabrègue, Ahmed Hassayoune, David Goncalves, Madeleine Hueber, Thaddée Delebarre, Julien Savatovsky, Clara Fonteneau, Charles Maussion, Bertrand Thirion, Alexis Thual
Abstract: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 underwent multiple scanning sessions, each consisting of five 15-minute runs, while listening to podcasts in their native language. With 30 hours of fMRI data per subject, the current release is the deepest available fMRI dataset using naturalistic speech stimuli. The dataset pairs brain activity with the corresponding podcast audio, transcript annotations, and derived stimulus embeddings. Furthermore, we introduce a brain decoding benchmark formulated as audio segment retrieval: given fMRI activity from a held-out session, the decoder must identify the corresponding time-aligned podcast audio segment among candidate segments. We provide standardized splits, evaluation metrics, and baseline decoders for this task. Finally, a scaling analysis shows that decoding performance improves continuously with the amount of training data per subject.
| Comments: | Accepted at NeurIPS 2026, Evaluations & Datasets Track |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.03558 [cs.LG] |
| (or arXiv:2610.03558v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03558 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Antoine Collas [view email]
[v1]
Fri, 2 Oct 2026 16:38:44 UTC (1,017 KB)
来源：arXiv：cs.LG · arxiv.org